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Sparse Modeling: Theory, Algorithms, and Applications de Irina Rish

Sparse Modeling: Theory, Algorithms, and Applications: Chapman & Hall/CRC Machine Learning & Pattern Recognition

Autor Irina Rish, Genady Grabarnik
en Limba Engleză Hardback – dec 2014
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.
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Specificații

ISBN-13: 9781439828694
ISBN-10: 1439828695
Pagini: 254
Ilustrații: 52 black & white illustrations, 7 colour illustrations
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.8 kg
Ediția:New.
Editura: CRC Press
Colecția CRC Press
Seria Chapman & Hall/CRC Machine Learning & Pattern Recognition

Public țintă

Professional Practice & Development

Cuprins

Introduction. Sparse Signal Recovery: Compressed Sensing. Sparse Optimization Algorithms. Sparse Regression. Sparse Graphical Models. Practical Applications. Beyond Variable Selection: Supervised Dimensionality Reduction.

Recenzii

"… 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

Descriere

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.