A Compressed Overview of Sparsity

This talk provides a high-level introduction to sparsity and compressed sensing for high-dimensional engineering and dynamical systems. It covers signal compression, sparse representations, L1 minimization, measurement strategies, reconstruction, and engineering applications.

Syllabus

A COMPRESSED OVERVIEW OF SPARSITY
Compression vs. Compressed Sensing
Pixel Space is (Larger Than) Astronomical
Reconstruction by Compressed Sensing
Beating Shannon-Nyquist
Robust Statistics and Outlier Rejection
A Compressed Summary of L1 Minimization
Why does L1 Minimization Promote Sparsity?
Ingredients of Compressed Sensing
Not Just Useful for Images