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

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

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

by Irina Rish; Genady Grabarnik

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pp. 254 1st edition NO-PA16APR2015-KAP. Papeback. New.
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  • Title Sparse Modeling: Theory, Algorithms, and Applications (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
  • Author Irina Rish; Genady Grabarnik
  • Binding Papeback
  • Condition New
  • Pages 236
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date pp. 254 1st edition NO-PA16
  • Features Bibliography
  • Bookseller's Inventory # 6379925020
  • ISBN 9780367658922 / 0367658925
  • Weight 0.8 lbs (0.36 kg)
  • Dimensions 9.21 x 6.14 x 0.54 in (23.39 x 15.60 x 1.37 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Business / Economics / Finance
  • Dewey Decimal Code 620.001
  • Quantity available 4

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From the publisher

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.

About the author

Irina Rish, Genady Grabarnik

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