Wednesday, August 31, 2011

Implementation of rONE-L1

Among the many things that got my interest in the following paper, here is the tidbit:

It is shown that rONE-L1 is faster than AMP and NESTA when the number of measurements is just enough to exactly reconstruct the original sparse signal using l_1 minimization.

wow. Faster than AMP, here is the attendant paper and code:

Orthonormal Expansion $\ell_1$-Minimization Algorithms for Compressed Sensing by Zai Yang, Cishen Zhang, Jun Deng, Wenmiao Lu. The abstract reads:
Compressed sensing aims at reconstructing sparse signals from significantly reduced number of samples, and a popular reconstruction approach is $\ell_1$-norm minimization. In this correspondence, a method called orthonormal expansion is presented to reformulate the basis pursuit problem for noiseless compressed sensing. Two algorithms are proposed based on convex optimization: one exactly solves the problem and the other is a relaxed version of the first one. The latter can be considered as a modified iterative soft thresholding algorithm and is easy to implement. Numerical simulation shows that, in dealing with noise-free measurements of sparse signals, the relaxed version is accurate, fast and competitive to the recent state-of-the-art algorithms. Its practical application is demonstrated in a more general case where signals of interest are approximately sparse and measurements are contaminated with noise.
The rOne-L1 code is here. Thanks Zai.

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Tuesday, August 30, 2011

Welcome to the GoDec algorithm, a new member of the Matrix Factorization Jungle

Welcome to a new member of the Matrix Factorization JungleTianyi Zhou just blogged and released an implementation of the GoDec algorithm for the Randomized Low-rank and Sparse Matrix Decomposition in the Noisy Case. The Go Decomposition site and attendant code is here. It looks very good, but I particularly dig the third step of the algorithm:

3. Develop randomized low-rank approximation method "bilateral random projection (BRP)" to further accelerate the update in the algorithm;
But the most important question is: In what star studded casino robbing movie are we going see the nerd explain in a one liner that the guards will only see the low rank version of the scene ? That's what I want to know.

The video of the ICML is on Tianyi's blog entry. The attendant ICML paper is : GoDec: Randomized Low-rank and Sparse Matrix Decomposition in Noisy Case by Tianyi Zhou and Dacheng Tao. The abstract reads:
Low-rank and sparse structures have been profoundly studied in matrix completion and compressed sensing. In this paper, we develop “Go Decomposition” (GoDec) to efficiently and robustly estimate the low-rank part L and the sparse part S of a matrix X = L + S + G with noise G. GoDec alternatively assigns the low-rank approximation of X − S to L and the sparse approximation of X − L to S. The algorithm can be significantly accelerated by bilateral random projections (BRP). We also propose GoDec for matrix completion as an important variant. We prove that the objective value ∥X − L − S∥2F converges to a local minimum, while L and S linearly converge to local optimums. Theoretically, we analyze the influence of L, S and G to the asymptotic/convergence speeds in order to discover the robustness of GoDec. Empirical studies suggest the efficiency, robustness and effectiveness of GoDec comparing with representative matrix decomposition and completion tools, e.g., Robust PCA and OptSpace
Thanks Tianyi !

GoDec has been included in the Matrix Factorization Jungle.

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The Compressive Sensing Handle

What are the connection between the different problems of finding a defective coin or coins within a large set of known non defective ones when there is:
It could be coins, precious rings or balls, they all ask the same question. How do you assemble some measurement process in order to recover an ugly duckling ? More important, all these riddles are a handle: The story reminds you

  • that the act of finding a defective coin is not as important as how you find it, 
  • that compressive sensing that can be made simple to describe and eventually map to a large set of fields.

Why is that important ? Being familiar with a particular problem sometimes hinders your ability to identify the big picture. You may have worked for years in coded aperture yet cannot make a clear connection between that and fertilizers and crops. You may know certain techniques with no firm theoretical grounding yet when you hear about compressive sensing, you know and find that there is indeed a connection. The handle acts as a Rosetta stone. One breakthrough in one field immediately translate into many other fields. Empirical methods that work all the time cannot remain empirical. Should you wait for this theoretical underpinning to come to fruition before you can publish ? No worry, the filter bubble of peer review make that decision for you

At a different level, one can also ask whether Marcel Kolodziejczyk's solution to the balance puzzle provides a new insight for better or faster sparse signal recovery solvers used in compressive sensing and other fields ? Maybe. Can one map the coin problem into a Sudoku problem ? it would be a interesting path.

What are the handles for the different matrix factorization techniques, structured sparsity norms that are springing up left and right ?

Two courses are offered this fall on compressive sensing and beyond that might help in developing this insight:

P.S:  Marcel worked out the 40 coin problem with four weightings, I didn't know somebody had done that.
P.P. S.: A recent entry on the connection between compressive sensing and group testing.
P.P.P.S: Terry Tao uses the same handle.

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Monday, August 29, 2011

Penalty Decomposition Methods for $L0$-Norm Minimization and Rank Minimization

This a little late, but as I was updating the Big Picture, I realized that I missed listing an implementation of a compressive sensing solver based on l_0 and a similar rank minimization solver. This is another cue that sparse signal recovery and matrix factorization share many insights. So without further due, here are the papers and attendant implementations.

In this paper we consider general l0-norm minimization problems, that is, the problems with l0-norm appearing in either objective function or constraint. In particular, we first reformulate the l0-norm constrained problem as an equivalent rank minimization problem and then apply the penalty decomposition (PD) method proposed in [33] to solve the latter problem. By utilizing the special structures, we then transform all matrix operations of this method to vector operations and obtain a PD method that only involves vector operations. Under some suitable assumptions, we establish that any accumulation point of the sequence generated by the PD method satisfies a first-order optimality condition that is generally stronger than one natural optimality condition. We further extend the PD method to solve the problem with the l0-norm appearing in objective function. Finally, we test the performance of our PD methods by applying them to compressed sensing, sparse logistic regression and sparse inverse covariance selection. The computational results demonstrate that our methods generally outperform the existing methods in terms of solution quality and/or speed.

The attendant MATLAB code is here.

In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first show that a class of matrix optimization problems can be solved as lower dimensional vector optimization problems. As a consequence, we establish that a class of rank minimization problems have closed form solutions. Using this result, we then propose penalty decomposition methods for general rank minimization problems in which each subproblem is solved by a block coordinate descend method. Under some suitable assumptions, we show that any accumulation point of the sequence generated by our method when applied to the rank constrained minimization problem is a stationary point of a nonlinear reformulation of the problem. Finally, we test the performance of our methods by applying them to matrix completion and nearest low-rank correlation matrix problems. The computational results demonstrate that our methods generally outperform the existing methods in terms of solution quality and/or speed.
 The attendant MATLAB code is here.

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Saturday, August 27, 2011

Tennis Players are Sparse !

While taking EE364b: Convex Optimization II with Stephen BoydIvan Papusha decided to do the following project:

... explores several large-scale methods for solving the Robust PCA problem via Principal Component Pursuit (PCP), a convex optimization problem, and demonstrates its use in background extraction tasks in video and point-cloud LIDAR data.

The idea is to perform background subtraction on a video stream. The PCP problem admits natural decomposition into Alternating Direction Method of Multipliers (ADMM, the course notes on ADMM are here) form. Here is the result: ‘‘Fast Automatic Background Extraction via Robust PCA’’. The poster is here. The matlab implementation is here.

Thanks Ivan  for sharing.

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Friday, August 26, 2011

Welcome to the Matrix Factorization Jungle

While we are beginning to see some good solvers out of the generic compressed sensing reconstruction solver front or let's call them sparse recovery solvers, a similar chaotic front is now appearing in front of our eyes in terms of Matrix Factorization. The discovery that the nuclear norm could serve as a proxy to the rank functional has led to a slew of different approaches that encompass older formulation like the PCA. Initially, one of the emphasis was to decrease the computational time to approximate the rank through the use of random projection and concentration of measure results as embodied in the Randomized PCA. But with functionals approximating the rank, we began to see other use of these concentration results stemming from the high dimensionality of our current data deluge . I am surely missing out on some approaches but here are the two schools of thoughts I have seen so far. On the one hand, we have folks looking to perform matrix decomposition such as:

A = L + S + N

where L is a low rank approximation, S is a sparse matrix of potentially large coefficients and N a "noisy matrix". This problem is called Principal Component Pursuit and it's little sister A = L + S is called Robust PCA. Many of these approaches tend to aim for solving the matrix completion issue that the Netflix problem was trying to solve. Beyond the camera surveillance problem, the rise of Facebook, LinkedIn and other some such players is invariably pushing for these "privacy interpolating" approaches. There are a number of issues with these decomposition, including whether or not the sparse components can themselves be considered low or high rank for instance. But I am sure the subject will be beaten to death and improved over time. 

Then there is the second school of thoughts around folks coming from a background of statistics who somehow want to perform Dictionary learning and Sparse Principal Component Analysis. Here we have:

M = U V^T

As one can see in this presentation slide, the L + S decomposition is also somehow included in this approach.

What I find fascinating here is that this train of thought eventually leads to defining new norms in order to expand on the whole structured sparsity concept. But as in any jungle, Sparse PCA, for instance does not even seem to have a unique definition (see expository slide: Methods for Sparse PCA by Daniela Witten)

If you want to learn more about this new jungle of opportunity, you may want to check the following excellent resources:

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Thursday, August 25, 2011

All is not well with the Nuclear norm world...

Echoing, Yonina Eldar's comment that the nuclear norm was not a very good proxy for the rank functional in the imaging business, here is another data point by Xiaoxiao Shi and Philip Yu in the matrix completion business. Maybe they should take a look at the log-det heuristics instead or avoid the proxy issue altogether? I like these negative results papers, however, one should not read too much into them, there are helpful warning signs for the busy person, but either the domain in which this algorithm is applied is not optimal or better techniques are known by others in the field. I wish it were said more forcefully though. Here is the paper: Limitations of Matrix Completion via Trace Norm Minimization by Xiaoxiao Shi and Philip S. Yu. The abstract reads:
In recent years, compressive sensing attracts intensive attentions in the field of statistics, automatic control, data mining and machine learning. It assumes the sparsity of the dataset and proposes that the whole dataset can be reconstructed by just observing a small set of samples. One of the important approaches of compressive sensing is trace norm minimization, which can minimize the rank of the data matrix under some conditions. For example, in collaborative filtering, we are given a small set of observed item ratings of some users and we want to predict the missing values in the rating matrix. It is assumed that the users' ratings are affected by only a few factors and the resulting rating matrix should be of low rank. In this paper, we analyze the issues related to trace norm minimization and find an unexpected result that trace norm minimization often does not work as well as expected.

Image Credit: NASA/JPL-Caltech/Cornell/ASU. Ridout' Rock on Rim of Odyssey Crater
NASA's Mars Exploration Rover Opportunity looked across a small crater on the rim of a much larger crater to capture this raw image from its panoramic camera during the rover's 2,685th Martian day, or sol, of work on Mars (Aug. 13, 2011).
Images and Captions

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Wednesday, August 24, 2011

That word you keep using .... [ENS/INRIA Summer school on Visual Recognition and Machine Learning]

From the recent ENS/INRIA Summer school on Visual Recognition and Machine Learning that took place in Paris, 25-29 July 2011. I note that Jean Ponce makes the point that sparse coding is not compressed sensing, a little bit like what Muthu was saying a while back. Obviously, becoming mainstream has its downs, but let me wonder something aloud. The whole field of computer vision is based on the underlying assumption that the transfer function of most camera equipment between a scene and its 2-D projection on the focal plane array is a simple one. What if it were a more complex one ? Would most of the techniques used in computer vision and image understanding also work or break down ? To me this is an essential question.

Here are the slides of the meeting, the materials for the practical sessions is here. Enjoy the learning experience.

Lecture slides

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Tuesday, August 23, 2011

Compressive Sensing Literature This Week -updated-

Today, we have a long entry and start with a hardware implementation of compressive sensing. Enjoy! : .Photon-counting compressive sensing laser radar for 3D imaging by Gregory HowlandPaul Dixon, and John Howell. The abstarct reads:

We experimentally demonstrate a photon-counting, single-pixel, laser radar camera for 3D imaging where transverse spatial resolution is obtained through compressive sensing without scanning. We use this technique to image through partially obscuring objects, such as camouflage netting. Our implementation improves upon pixel-array based designs with a compact, resource-efficient design and highly scalable resolution.

Recovery of Sparse Signals from Noisy Measurements Using an l`p-Regularized Least-Squares Algorithm by J. K. Pant, Wu-Sheng. Lu, and Andrea Antoniou.

What I note from this presentation is that SL0, a very simple code, provides very similar capabilities and is much faster than this reweighted lp solver. 

Not related to CS per se, but I found the presentation by one of the author of this paper intriguing, in part because it would seem to me that CS should be all over this field:

In them, there is a talk about a 2pi/3 period which introduces well the next paper:

Spectral Compressive Sensing by Marco DuarteRichard Baraniuk. The abstract reads:
Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a
signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins; when this is not the case, CS recovery performance degrades signi cantly. In this paper, we introduce the spectral CS (SCS) recovery framework for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter estimation algorithms from the eld of spectral estimation. Using periodogram and line spectral estimation methods (speci cally Thomson's multitaper method and MUSIC), we demonstrate that SCS signi cantly outperforms current state-of-the-art CS algorithms based on the DFT while providing provable bounds on the number of measurements required for stable recovery
This is an update to a previous preprint. As usual, the code for SCS is available in the true spirit of reproducible results.

In di ffusion MRI (dMRI) domain, many High Angular Resolution Di usion Imaging (HARDI) methods were proposed to estimate Ensemble Average Propagator (EAP) and Orientation Distribution Function (ODF). They normally need many samples, which limits their applications. Some Compressive Sensing (CS) based methods were proposed to estimate ODF in Q-Ball Imaging (QBI) from limited samples. However EAP estimation is much more difficult than ODF in QBI. Recently Spherical Polar Fourier Imaging (SPFI) was proposed to represent di usion signal using Spherical Polar Fourier (SPF) basis without specific assumption on di ffusion signals and analytically obtain EAP and ODF via the Fourier dual SPF (dSPF) basis from arbitrarily sampled signal. Normally the coecients of SPF basis are estimated via Least Square with weighted `2 norm regularization (`2-SPFI). However, `2-SPFI needs a truncated basis to avoid overfitting, which brings some estimation errors. By considering the Fourier relationship between EAP and signal and the Fourier basis pair provided in SPFI, we propose a novel EAP estimation method, named `1-SPFI, to estimate EAP from limited samples using CS technique, and favorably compare it to the classical `2-SPFI method. `1-SPFI estimates the coecients in SPFI using least square with weighted `1 norm regularization. The weights are designed to enhance the sparsity. `1-SPFI significantly accelerates the ordinary CS based Fourier reconstruction method. This is performed by using SPF basis pair in CS estimation process which avoids the numerical Fourier transform in each iteration step. By considering high order basis in `1 optimization, `1-SPFI improves EAP reconstruction especially for the angular resolution. The proposed `1-SPFI was validated by synthetic, phantom and real data. The CS EAP and ODF estimations are discussed in detail and we show that recovering the angular information from CS EAP requires much less samples than exact CS EAP reconstruction. Various experiments on synthetic, phantom and real data validate the fact that SPF basis can sparsely represent DW-MRI signals and `1-SPFI largely improves the CS EAP reconstruction especially the angular resolution.

Since Diffusion Tensor Imaging (DTI) cannot detect the fiber crossing, many new works beyond DTI has been proposed to explore the q-space. Most works, known as single shell High Angular Resolution Imaging (sHARDI), focus on single shell sampling and reconstruct the Orientation Distribution Function (ODF). The ODF, which has no radial information at all, is just one of features of Ensemble Average Propagator (EAP). Diffusion Spectrum Imaging (DSI) is a standard method to estimate EAP via numerical Fourier Transform (FT), which needs lots of samples and is impractical for clinical study. Spherical Polar Fourier Imaging (SPFI) [1,2] was proposed to represent the signal using SPF basis, then the EAP and the ODF have analytical closed forms. So the estimation of the coefficients under SPF basis is very important. In [1,2], the coefficients are estimated based on a standard Least Square (LS) with L2 norm regularization (L2-L2). In this paper, we propose to estimate the coefficients using LS with L1 norm regularization (L2-L1), also named as Least Absolute Selection and Shrinkage Operator (LASSO). And we prove that the L2-L1 estimation of the coefficients is actually the well known Compressive Sensing (CS) method to estimate EAP, which brings lots of Mathematical tools and possibility to improve the sampling scheme in q-space

Compressive Sampling for Power Spectrum Estimation by Dyonisius Dony Ariananda, Geert Leus. The abstract reads:
Compressive sampling is a well-known approach to reconstruct sparse signals based on a limited number of measurements. In spectrum sensing applications for cognitive radio though, only reconstruction of the power spectrum of the signal is required, instead of the signal itself. In this paper, we present a new method for power spectrum reconstruction based on samples produced by a sub-Nyquist rate sampling device. The stationary assumption on the received analog signal causes the measurements at the output of the compressive sampling block to be cyclo-stationary, or the measurement vectors to be stationary. We investigate the relationship between the autocorrelation matrix of the measurement vectors and that of the received analog signal, which we represent by its Nyquist rate sampled version. Based on this relationship, we are able to express the autocorrelation sequence of the received wide sense stationary signal as a linear function of the vectorized autocorrelation matrix of the measurement vectors. Depending on the compression rate, we can present the problem as either over-determined or under-determined. Our focus will be mainly on the over-determined case, in which the reconstruction does not require any additional constraints. Two types of sampling matrices are examined, namely complex Gaussian and multi-coset sampling matrices. For both of them, we can derive conditions under which the over-determined system will result in a unique solution for the power spectrum by adopting a simple least squares (LS) algorithm. In the case of multi-coset sampling, further improvement on the quality of the power spectrum estimates can be attained by optimizing the condition of the sampling matrix.
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate non-smooth norms. The goal of this paper is to present from a general perspective optimization tools and techniques dedicated to such sparsity-inducing penalties. We cover proximal methods, block-coordinate descent, reweighted ℓ2-penalized techniques, working-set and homotopy methods, as well as nonconvex formulations and extensions, and provide an extensive set of experiments to compare various algorithms from a computational point of view.

Separation-based Joint Decoding in Compressive Sensing by Hsieh-Chung Chen and H. T. Kung. The abstract reads:

We introduce a joint decoding method for compressive sensing that can simultaneously exploit sparsity of individual components of a composite signal. Our method can significantly reduce the total number of variables decoded jointly by separating variables of large magnitudes in one domain and using only these variables to represent the domain. Furthermore, we enhance the separation accuracy by using joint decoding across multiple domains iteratively. This separation-based approach improves the decoding time and quality of the recovered signal. We demonstrate these benefits analytically and by presenting empirical results.

Identifying Bad Measurements in Compressive Sensing by  H.T. Kung, Tsung-Han LinDario Vlah. The abstract reads:
We consider the problem of identifying bad measurements in compressive sensing. These bad measurements can be present due to malicious attacks and system malfunction. Since the system of linear equations in compressive sensing is underconstrained, errors introduced by these bad measurements can result in large changes in decoded solutions. We describe methods for identifying bad measurements so that they can be removed before decoding. In a new separation-based method we separate out top nonzero variables by ranking, eliminate the remaining variables from the system of equations, and then solve the reduced overconstrained problem to identify bad measurements. Comparing to prior methods based on direct or joint ℓ1-minimization, the separation-based method can work under a much smaller number of measurements. In analyzing the method we introduce the notion of inversions which governs the separability of large nonzero variables.

CloudSense: Continuous Fine-Grain Cloud Monitoring With Compressive Sensing by  H.T. Kung, Chit-Kwan Lin and Dario Vlah. The abstract reads:
Continuous fine-grain status monitoring of a cloud data center enables rapid response to anomalies, but handling the resulting torrent of data poses a significant challenge. As a solution, we propose CloudSense, a new switch design that performs in-network compression of status streams via compressive sensing. Using MapReduce straggler detection as an example of cloud monitoring, we give evidence that CloudSense allows earlier detection of stragglers, since finer-grain status can be reported for a given bandwidth budget. Furthermore, CloudSense showcases the advantage of an intrinsic property of compressive sensing decoding that enables detection of the slowest stragglers first. Finally, CloudSense achieves in-network compression via a low-complexity encoding scheme, which is easy and convenient to implement in a switch. We envision that CloudSense switches could form the foundation of a “compressed status information plane” that is useful for monitoring not only the cloud data center itself, but also the user applications that it hosts.

Partitioned Compressive Sensing with Neighbor-Weighted Decoding by H.T. Kung and Stephen J. Tarsa. The abstract reads:
Compressive sensing has gained momentum in recent years as an exciting new theory in signal processing with several useful applications. It states that signals known to have a sparse representation may be encoded and later reconstructed using a small number of measurements, approximately proportional to the signal’s sparsity rather than its size. This paper addresses a critical problem that arises when scaling compressive sensing to signals of large length: that the time required for decoding becomes prohibitively long, and that decoding is not easily parallelized. We describe a method for partitioned compressive sensing, by which we divide a large signal into smaller blocks that may be decoded in parallel. However, since this process requires a significant increase in the number of measurements needed for exact signal reconstruction, we focus on mitigating artifacts that arise due to partitioning in approximately reconstructed signals. Given an error-prone partitioned decoding, we use large magnitude components that are detected with highest accuracy to influence the decoding of neighboring blocks, and call this approach neighbor-weighted decoding. We show that, for applications with a predefined error threshold, our method can be used in conjunction with partitioned compressive sensing to improve decoding speed, requiring fewer additional measurements than unweighted or locally-weighted decoding.

Collaborative Compressive Spectrum Sensing in a UAV Environment by  Hsieh-Chung ChenH. T. Kung, ,  Dario Vlah, Daniel Hague, Michael Muccio and Brendon Poland. The abstract reads:
Spectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation and network bandwidth when the bands under scanning are wide and highly contested. In this paper we propose distributed spectrum sensing with multiple sensing nodes in a UAV environment. The ground nodes in our scheme sense the spectrum in parallel using compressive sensing. Each sensor node transmits compressive measurements to a nearby UAV in the air. The UAV performs decoding on the received measurements; it decodes information with increasing resolution as it receives more measurements. Furthermore, by a property of compressive sensing decoding, frequencies of large magnitude responses are recovered first. In the proposed scheme, as soon as the UAV detects the presence of such high-power frequencies from a sensor, this information is used to aid decoding for other sensors. We argue that such collaboration enabled by UAV will greatly enhance the decoding accuracy of compressive sensing. We use packet-loss traces acquired in UAV flight experiments in the field, as well as field experiments involving software-defined radios, to validate the effectiveness of this distributed compressive sensing approach

Measurement Combining and Progressive Reconstruction in Compressive Sensing by Hsieh-Chung Chen, H. T. Kung, Dario Vlah, Bruce Suter. The abstract reads:
Compressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost of compressive sensing, however, grows superlinearly with the problem size. In distributed sensor systems, the aggregate amount of compressive measurements encoded by the sensors can be substantial, and the decode cost for all the variables involved can be large. In this paper we propose a method to combine measurements from distributed sensors. With our method we can transport and store a single combined measurement set, rather than multiple sets for all sensors. We show that via source separation and joint decoding, it is possible to recover an approximate to the original signal from combined measurements using progressive reconstruction which focuses on individual sensors. This results in a reduction in the number of variables used in decoding and consequently a reduced decoding time. We show that the computed approximation to the signal can still have sufficient accuracy for target detection. We describe the combining approach and the associated progressive reconstruction, and we illustrate them with image recovery for simple target detection examples.

Asymptotic Analysis of Complex LASSO via Complex Approximate Message Passing (CAMP) by Arian Maleki, Laura Anitori, Zai Yang, Richard Baraniuk. The abstract reads:

Recovering a sparse signal from an undersampled set of random linear measurements is the main problem of interest in compressed sensing. In this paper, we consider the case where both the signal and the measurements are complex. We study the popular reconstruction method of $\ell_1$-regularized least squares or LASSO. While several studies have shown that the LASSO algorithm offers desirable solutions under certain conditions, the precise asymptotic performance of this algorithm in the complex setting is not yet known. In this paper, we extend the approximate message passing (AMP) algorithm to the complex signals and measurements and obtain the complex approximate message passing algorithm (CAMP). We then generalize the state evolution framework recently introduced for the analysis of AMP, to the complex setting. Using the state evolution, we derive accurate formulas for the phase transition and noise sensitivity of both LASSO and CAMP.

Sparse Recovery with Graph Constraints: Fundamental Limits and Measurement Construction by Meng Wang, Weiyu Xu, Enrique Mallada, Ao Tang. The abstract reads:
This paper addresses the problem of sparse recovery with graph constraints in the sense that we can take additive measurements over nodes only if they induce a connected subgraph. We provide explicit measurement constructions for several special graphs. A general measurement construction algorithm is also proposed and evaluated. For any given graph $G$ with $n$ nodes, we derive order optimal upper bounds of the minimum number of measurements needed to recover any $k$-sparse vector over $G$ ($M^G_{k,n}$). Our study suggests that $M^G_{k,n}$ may serve as a graph connectivity metric.

Sparsity without the Complexity: Loss Localisation using Tree Measurements by Vijay Arya, Darryl Veitch. The abstract reads:
We study network loss tomography based on observing average loss rates over a set of paths forming a tree -- an ill-conditioned linear problem for the link loss probabilities. We examine in detail the role of sparsity as a regularising principle, pointing out that the problem is technically distinct from others in the compressed sensing literature. We exploit the tree structure to derive sufficient conditions for sparse solutions to be unique, and present a fast single-pass linear algorithm which outputs the minimal $\ell_1$ solution, which we prove is always unique, and always has the minimal $\ell_0$ (sparsity). By considering the placement of lossy links within the tree, we show that sparse solutions remain unique much more often than is commonly supposed. We prove similar results for a noisy version of the problem.
Advanced phase retrieval: maximum likelihood technique with sparse regularization of phase and amplitude by Artem Migukin, Vladimir Katkovnik, Jaakko Astola. The abstract reads:
Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase. The algorithm is derived in terms of a constrained maximum likelihood, where the wave field reconstruction is performed using a number of noisy intensity-only observations with a zero-mean additive Gaussian noise. The developed algorithm enables the optimal solution for the object wave field reconstruction. Our goal is an improvement of the reconstruction quality with respect to the conventional algorithms. Sparse regularization results in advanced reconstruction accuracy, and numerical simulations demonstrate significant enhancement of imaging.

Structured Sparsity and Generalization by Andreas Maurer, Massimiliano Pontil. The abstract reads:
We present a data dependent generalization bound for a large class of regularized algorithms which implement structured sparsity constraints. The bound can be applied to standard squared-norm regularization, the lasso, the group lasso, some versions of the group lasso with overlapping groups, multiple kernel learning and other regularization schemes. In all these cases competitive results are obtained. A novel feature of our bound is that it can be applied in an infinite dimensional setting such as the lasso in a separable Hilbert space or multiple kernel learning with a countable number of kernels.

Exact Reconstruction Conditions for Regularized Modified Basis Pursuit by Wei Lu, Namrata Vaswani. The abstract reads:
In this correspondence, we obtain exact recovery conditions for regularized modified basis pursuit (reg-mod-BP) and discuss when the obtained conditions are weaker than those for modified-CS or for basis pursuit (BP). The discussion is also supported by simulation comparisons. Reg-mod-BP provides a solution to the sparse recovery problem when both an erroneous estimate of the signal's support, denoted by $T$, and an erroneous estimate of the signal values on $T$ are available.

Symmetric Group Testing and Superimposed Codes by Amin Emad, Jun Shen, Olgica Milenkovic. The abstract reads:
We describe a generalization of the group testing problem termed symmetric group testing. Unlike in classical binary group testing, the roles played by the input symbols zero and one are "symmetric" while the outputs are drawn from a ternary alphabet. Using an information-theoretic approach, we derive sufficient and necessary conditions for the number of tests required for noise-free and noisy reconstructions. Furthermore, we extend the notion of disjunct (zero-false-drop) and separable (uniquely decipherable) codes to the case of symmetric group testing. For the new family of codes, we derive bounds on their size based on probabilistic methods, and provide construction methods based on coding theoretic ideas.

Differential Phase-contrast Interior Tomography by Wenxiang Cong, Ge Wang. The abstract reads:
Differential phase contrast interior tomography allows for reconstruction of a refractive index distribution over a region of interest (ROI) for visualization and analysis of internal structures inside a large biological specimen. In this imaging mode, x-ray beams target the ROI with a narrow beam aperture, offering more imaging flexibility at less ionizing radiation. Inspired by recently developed compressive sensing theory, in numerical analysis framework, we prove that exact interior reconstruction can be achieved on an ROI via the total variation minimization from truncated differential projection data through the ROI, assuming a piecewise constant distribution of the refractive index in the ROI. Then, we develop an iterative algorithm for the interior reconstruction and perform numerical simulation experiments to demonstrate the feasibility of our proposed approach.

. Recovering compressively sampled signals using partial support information by Michael P. Friedlander, Hassan Mansour, Rayan Saab, Ozgur Yilmaz. The abstract reads:

We study recovery conditions of weighted !1 minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that if at least 50% of the (partial) support information is accurate, then weighted !1 minimization is stable and robust under weaker sufficient conditions than the analogous conditions for standard !1 minimization. Moreover, weighted !1 minimization provides better upper bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal to be recovered. We illustrate our results with extensive numerical experiments on synthetic data and real audio and video signals.

Several presentation from Felix J. Herrmann's group:

By the way, this use of the Student's t-distribution is something I have heard about on sparse approximation from Aleks Jakulin a while back on Andrew's Statistical Modeling, Causal Inference, and Social Science blog. Of related interest is the mention of Andrew's paper back in 1992.

Convex Approaches to Model Wavelet Sparsity Patterns by Nikhil S. Rao, Robert D. NowakStephen J. WrightNick G. Kingsbury. The abstract reads
Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in deconvolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso.
And finally, we have a list of abstracts only::

One-step-ahead Kinematic Compressive Sensing by Hover F., R. Hummel, U. Mitra, and G. Sukhatme. The abstract reads:
A large portion of work on compressive sampling and sensing has focused on reconstructions from a given measurement set. When the individual samples are expensive and optional, as is the case with autonomous agents operating in a physical domain and under specic energy limits, the CS problem takes on a new aspect because the projection is column-sparse, and the number of samples is not necessarily large. As a result, random sampling may no longer be the best tactic. The underlying incoherence properties in ℓ0 reconstruction, however, can still motivate the purposeful design of samples in planning for CS with one or more agents; we develop here a greedy and computationally tractable sampling rule that will improve errors relative to random points. Several example cases illustrate that the approach is e ective and robust.

Accelerated diffusion spectrum imaging in the human brain using compressed sensing by Marion I. Menzel1,*, Ek T. Tan2, Kedar Khare2, Jonathan I. Sperl1, Kevin F. King3, Xiaodong Tao2, Christopher J. Hardy2, Luca Marinelli2. The abstract reads:
We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4.

A compressed sensing-based iterative algorithm for CT reconstruction and its possible application to phase contrast imaging by Li X, Luo S. The abstract reads:
Computed Tomography (CT) is a technology that obtains the tomogram of the observed objects. In real-world applications, especially the biomedical applications, lower radiation dose have been constantly pursued. To shorten scanning time and reduce radiation dose, one can decrease X-ray exposure time at each projection view or decrease the number of projections. Until quite recently, the traditional filtered back projection (FBP) method has been commonly exploited in CT image reconstruction. Applying the FBP method requires using a large amount of projection data. Especially when the exposure speed is limited by the mechanical characteristic of the imaging facilities, using FBP method may prolong scanning time and cumulate with a high dose of radiation consequently damaging the biological specimens.

Fast Approximate Text Document Clustering Using Compressive Sampling by Laurence A. F. Park. The abstract reads:

Document clustering involves repetitive scanning of a document set, therefore as the size of the set increases, the time required for the clustering task increases and may even become impossible due to computational constraints. Compressive sampling is a feature sampling technique that allows us to perfectly reconstruct a vector from a small number of samples, provided that the vector is sparse in some known domain. In this article, we apply the theory behind compressive sampling to the document clustering problem using k-means clustering. We provide a method of computing high accuracy clusters in a fraction of the time it would have taken by directly clustering the documents. This is performed by using the Discrete Fourier Transform and the Discrete Cosine Transform. We provide empirical results showing that compressive sampling provides a 14 times increase in speed with little reduction in accuracy on 7,095 documents, and we also provide a very accurate clustering of a 231,219 document set, providing 20 times increase in speed when compared to performing k-means clustering on the document set. This shows that compressive clustering is a very useful tool that can be used to quickly compute approximate clusters.

Applying Compressive Sampling Theory Successfully to a Wideband Cooperative Spectrum Sensing Scheme by Duan Jiaqi,Li Yong

The introduction of the full paper reviews Refs.1 through 6 and then proposes the research of this paper,which is explained in sections 1,2,and 3.Their core is: " In the ideal cognitive radio(CR) systems,CR users are capable of sensing wide band spectrum simultaneously.However,due to the physical limits of front end devices,the real time entire spectrum sensing is difficult to be actualized.In order to tackle this problem,we propose a wide band spectrum sensing approach based on compressive sampling theory.First,we design a centralized multi-user parallel compressive sampling framework which realizes the high speed analog signal sampling of wide band spectrum sensing through low speed AD equipped CR users.Moreover,according to the compressive sampling theory,the proposed approach sufficiently utilizes the sparsity of primary signal on frequency domain.Thus,the base station can recover the frequency domain information of primary user by far less samples than Nyquist rate.Finally,the spectrum occupancy status can be determined by the recovery information." The simulation results,presented in Figs.3 and 4,reveal preliminarily that:(1) the proposed method has the higher detection probability and shorter sensing time than energy detection;(2) it needs only 10% memory units compared to conventional sensing schemes.

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Monday, August 22, 2011

Turbo-AMP: Compressive Imaging using Approximate Message Passing and a Markov-Tree Prior

We propose a novel algorithm for compressive imaging that exploits both the sparsity and persistence across scales found in the 2D wavelet transform coefficients of natural images. Like other recent works, we model wavelet structure using a hidden Markov tree (HMT) but, unlike other works, ours is based on loopy belief propagation (LBP). For LBP, we adopt a recently proposed "turbo" message passing schedule that alternates between exploitation of HMT structure and exploitation of compressive-measurement structure. For the latter, we leverage Donoho, Maleki, and Montanari's recently proposed approximate message passing (AMP) algorithm. Experiments with a large image database suggest that, relative to existing schemes, our turbo LBP approach yields state-of-the-art reconstruction performance with substantial reduction in complexity.

The code and attendant example of Turbo-AMP can be found on this site.

Friday, August 19, 2011