Videos: Structured Regularization for High-Dimensional Data Analysis: Compressed Sensing: Structure and Imaging & Matrix and graph estimation

The Summer School "Structured Regularization for High-Dimensional Data Analysis" is on-going in Paris right now at IHP Paris. Here are some of the videos of the first day (Monday) with Anders Hansen and Andrea Montanari

Lectures 1: Compressed Sensing: Structure and Imaging

   

 Lectures 2: Compressed Sensing: Structure and Imaging Anders Hansen (Cambridge) 

Lectures 1 and 2: Compressed Sensing: Structure and Imaging Abstract: The above heading is the title of a new book to be published by Cambridge University Press. In these lectures I will cover some of the main issues discussed in this monograph/textbook. In particular, we will discuss how the key to the success of compressed sensing applied in imaging lies in the structure. For example images are not just sparse in an X-let expansion, they have a very specific sparsity structure in levels according to the X-let scales. Similarly, when considering Total Variation, the gradient coefficients are also highly structured. Moreover, in most realistic sampling scenarios, the sampling operator combined with any X-let transform yields a matrix with a very specific coherence structure. The key to successfully use compressed sensing is therefore to understand how to utilise these structures in an optimal way, in particular in the sampling procedure. In addition, as the coherence and sparsity structures have very particular asymptotic behaviour, the performance of compressed sensing varies greatly with dimension, and so does the optimal way of sampling. Fortunately, there is now a developed theory that can guide the user in detail on how to optimise the use of compressed sensing in inverse and imaging problems. I will cover several of the key aspects of the theory accompanied with real-world examples from Magnetic Resonance Imaging (MRI), Nuclear Magnetic Resonance (NMR), Surface Scattering, Electron Microscopy, Fluorescence Microscopy etc. Recommended readings: (lectures 1 and 2) Chapter 4, 6 and 12 in “A mathematical introduction to compressed sensing” (Foucard/Rauhut) Breaking the coherence barrier: A new theory for compressed sensing On asymptotic structure in compressed sensing Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames

   

Andrea Montanari (Stanford): Matrix and graph estimation 

 Abstract: Many statistics and unsupervised learning problems can be formalized as estimating a structured matrix or a graph from noisy or incomplete observations. These problems present a large variety of challenges, and an intriguing interplay between computational and statistical barriers. I will provide an introduction to recent work in the area, with an emphasis on general methods and unifying themes. 1) Random matrix theory and spectral methods. 2) The semidefinite programming approach to graph clustering. 3) Local algorithms and graphical models. The hidden clique problem. 4) Non-negative matrix factorization.

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