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Machine Learning by Sergios Theodoridis

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Chapter 9

Sparsity-Aware Learning

Concepts and Theoretical Foundations

Abstract

This chapter presents the main concepts and theoretical foundations related to sparsity-aware learning techniques. The concept of sparse modeling is introduced together with the LASSO and the 0 and 1 norm minimizing tasks. Conditions for uniqueness of the obtained solutions as well as for the equivalence of the 0 and 1 norm minimization are stated. The RIP condition and related bounds are discussed. Compressed sensing and the notion of stable embeddings are reviewed. The concept of sub-Nyquist sampling is presented and finally a case study concerning image de-nosing is reported.

Keywords

Sparse modeling

LASSO

Basis pursuit

0 and 1 norms

Spark

Mutual coherence ...

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