Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing.Sparse Modeling: Theory, Algorithms, and Applications provides an introduction t

chapter 1|14 pages


chapter 2|20 pages

Sparse Recovery: Problem Formulations

chapter 3|18 pages

Theoretical Results (Deterministic Part)

chapter 4|18 pages

Theoretical Results (Probabilistic Part)

chapter 5|24 pages

Algorithms for Sparse Recovery Problems

chapter 6|26 pages

Beyond LASSO: Structured Sparsity

chapter 7|18 pages

Beyond LASSO: Other Loss Functions

chapter 8|28 pages

Sparse Graphical Models