Sparse Modeling - Theory, Algorithms, and Applications

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.

List of contents

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.

About the author

Irina Rish, Genady Grabarnik

Product details

• Authors: Genady Grabarnik, Grabarnik Genady, Irina Rish, Irina (Ibm Rish, Irina Grabarnik Rish, Rish Irina
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 01.12.2014
• EAN: 9781439828694
• ISBN: 978-1-4398-2869-4
• No. of pages: 254
• Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Business > General, dictionaries; BUSINESS & ECONOMICS / Statistics, TECHNOLOGY & ENGINEERING / Electrical, COMPUTERS / Programming / Games, COMPUTERS / Machine Theory, Electrical Engineering, Probability & statistics, Automatic control engineering, Communications engineering / telecommunications, Probability and statistics