Irina Rish, Genady Grabarnik

Sparse Modeling

Theory, Algorithms, and Applications

• Gebundenes Buch

Produktbeschreibung

Sparse modeling is an important issue in many applications of machine learning and statistics where the main objective is discovering predictive patterns in data to enhance understanding of underlying physical, biological, and other natural processes. This book surveys recent advances in statistics, machine learning, and signal processing related to sparse modeling. It provides a comprehensive introduction to recent developments in sparse modeling research, including the theoretical basis for sparse modeling, algorithmic approaches, and applications to computational biology, medicine, neuroscience, graphical model selection, and compressed sensing.

Produktdetails

• Verlag: CRC Press
• 1. Auflage
• Seitenzahl: 254
• Erscheinungstermin: 4. Dezember 2014
• Englisch
• Abmessung: 236mm x 155mm x 18mm
• Gewicht: 499g
• ISBN-13: 9781439828694
• ISBN-10: 1439828695
• Artikelnr.: 29378040

Autorenporträt

Irina Rish, Genady Grabarnik

Inhaltsangabe

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.

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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.

Rezensionen

"... 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 - École Normale Supérieure, Paris