Sparse Modeling
Theory, Algorithms, and Applications
1st Edition
Published on 1. December 2014
253 pages
978-1-040-06793-2 (ISBN)
Description
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
Reviews / Votes
"... an excellent introductory book for branching off into aspects of sparse modeling; it is also good for advanced students since it is contains an appendix with some of the mathematical background needed to learn from this book, including topics such as eigentheory, discrete Fourier transform, and subgaussian random variables. I very much recommend this book for researchers and students alike."-Computing Reviews, May 2015
"A comprehensive, clear, and well-articulated book on sparse modeling. This book will stand as a prime reference to the research community for many years to come."
-Ricardo Vilalta, Department of Computer Science, University of Houston
"This book provides a modern introduction to sparse methods for machine learning and signal processing, with a comprehensive treatment of both theory and algorithms. Sparse Modeling is an ideal book for a first-year graduate course."
-Francis Bach, INRIA - Ecole Normale Superieure, Paris
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
ISBN-13
978-1-040-06793-2 (9781040067932)
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Schweitzer Classification
Other editions
Additional editions
Book
09/2020
1st Edition
CRC Press
€69.40
Shipment within 15-20 days
Book
12/2014
1st Edition
CRC Press
€135.10
Shipment within 15-20 days
Content
Introduction. Sparse Recovery: Problem Formulations. Theoretical Results (Deterministic Part). Theoretical Results (Probabilistic Part). Algorithms for Sparse Recovery Problems. Beyond LASSO: Structured Sparsity. Beyond LASSO: Other Loss Functions. Sparse Graphical Models. Sparse Matrix Factorization: Dictionary Learning and Beyond. Epilogue. Appendix. Bibliography. Index.
