CS: CS related ICML'09 videos, Distributed Spatio-Temporal Sampling of Diffusion Fields from Sparse Instantaneous Sources, Smart-Sample

The ICML'09 videos are out . Out of the ones relevant to some aspect of compressive sensing here is a sample:

• Online Dictionary Learning for Sparse Coding by Julien Mairal
• Gradient Descent with Sparsification: An Iterative Algorithm for Sparse Recovery with Restricted Isometry Property by Rahul Garg
• Convex Variational Bayesian Inference for Large Scale Generalized Linear Models by Hannes Nickisch
• An Efficient Sparse Metric Learning in High-Dimensional Space via L1-Penalized Log-Determinant Regularization by Guo-Jun Qi
• Convex Sparse Methods for Feature Hierarchies by Francis R. Bach
• Unsupervised Discovery of Structure, Succinct Representations and Sparsity by Andrew Y. Ng
• Reinforcement Learning, Apprenticeship Learning and Robotic Control by Andrew Y. Ng

I am sure I am missing some. If so please do let me know. In the meantime, I'll add them to the Compressive Sensing Videos/Online Talks page. I also found two papers of interest:Distributed Spatio-Temporal Sampling of Diffusion Fields from Sparse Instantaneous Sources by Yue M. Lu and Martin Vetterli. The abstract reads:

We study the spatio-temporal sampling of a diffusion field driven by K unknown instantaneous source distributions. Exploiting the spatio-temporal correlation offered by the diffusion model, we show that it is possible to compensate for insufficient spatial sampling densities (i.e. sub-Nyquist sampling) by increasing the temporal sampling rate, as long as their product remains roughly a constant. Combining a distributed sparse sampling scheme and an adaptive feedback mechanism, the proposed sampling algorithm can accurately and efficiently estimate the unknown sources and reconstruct the field. The total number of samples to be transmitted through the network is roughly equal to the number of degrees of freedom of the field, plus some additional costs for in-network averaging.

and Smart-Sample: An Ecient Algorithm for Clustering Large High-Dimensional Datasets by Dudu Lazarov, Gil David, Amir Averbuch. The abstract reads:

Finding useful related patterns in a dataset is an important task in many interesting applications. In particular, one common need in many algorithms, is the ability to separate a given dataset into a small number of clusters. Each cluster represents a subset of data-points from the dataset, which are considered similar. In some cases, it is also necessary to distinguish data points that are not part of a pattern from the other data-points. This paper introduces a new data clustering method named smart-sample and compares its performance to several clustering methodologies. We show that smart-sample clusters successfully large high-dimensional datasets. In addition, smart-sample out-performs other methodologies in terms of running-time.

A variation of the smart-sample algorithm, which guarantees eciency in terms of I/O, is also presented. We describe how to achieve an approximation of the in-memory smart-sample algorithm using a constant number of scans with a single sort operation on the disk.