Sparse Modeling: Theory, Algorithms, and Applications

Irina Rish, Genady Grabarnik

December 1, 2014 by CRC Press
Reference - 253 Pages - 7 Color & 52 B/W Illustrations
ISBN 9781439828694 - CAT# K11409
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition


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  • Presents an introduction to the key concepts and major results in sparse modeling and signal recovery
  • Covers basic theoretical aspects of sparse modeling, state-of-the-art algorithmic approaches, and practical applications
  • Describes popular sparsity-enforcing approaches, such as l0- and l1-norm minimization
  • Explores several fast-developing subareas of sparse modeling, such as sparse Gaussian Markov random fields, structured sparsity, dictionary learning, and sparse matrix factorizations


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 to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field.

The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions.

The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.

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