E9 203: Compressed Sensing and Sparse Signal Processing: Video Lectures and Notes

Lecture 1: Introduction to underdetermined linear systems, penalty functions, l1 minimization, and linear programming. Video, Notes-SVG, Notes-PDF.

Lecture 2: Best s-term approximation, and why lp-ball with p < 1 promotes sparsity. Video1, Video2,Notes-SVG, Notes-PDF.

Lecture 3: Tighter bounds on compressible signals, minimal number of measurements for unique sparse vector recovery. Video, Notes-SVG, Notes-PDF.

Lecture 4: Minimal number of measurements for the recovery of all s-sparse vectors. Video, Notes-SVG, Notes-PDF.

Lecture 5: Recovery of individual sparse vectors. NP-hardness of l0 minimization. Video, Notes-SVG, Notes-PDF.

Lecture 6: L1 minimization leads to sparse solutions. Video, Notes-SVG, Notes-PDF.

Lecture 7: The orthogonal matching pursuit algorithm. Video, Notes-SVG, Notes-PDF.

Lecture 8: Thresholding based algorithms. Video, Notes-SVG, Notes-PDF.

Lecture 9: Regularization based methods. Extreme points, basic feasible solutions, and concave optimization. Video, Notes-SVG, Notes-PDF.

Lecture 10: Majorization-minimization based methods. Video, Notes-SVG, Notes-PDF.

Lecture 11: Reweighting based methods. Analysis of local minima. Video, Notes-SVG, Notes-PDF.

Lecture 12: Convergence of reweighting based methods. Video, Notes-SVG, Notes-PDF.

Lecture 13: Sparse Bayesian learning. Video, Notes-SVG, Notes-PDF.

Lecture 14: Sparse Bayesian learning - continued. Video, Notes-SVG, Notes-PDF.

Lecture 15: Discussion on the SBL prior, reweighted algorithms for SBL. Video, Notes-SVG, Notes-PDF.