Sparse modeling : theory, algorithms, and applications / Irina Rish, IBM, Yorktown Heights, New York, USA, Genady Ya. Grabarnik, St. John's University, Queens, New York, USA.

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublisher: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2015]Copyright date: ©2015Description: xviii, 231 pages : illustrations (some color) ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781439828694 (Hardback)
  • 1439828695 (Hardback)
Subject(s): Additional physical formats: Electronic version:: No titleDDC classification:
  • 511/.8 23
LOC classification:
  • TA342 .R57 2015
Available additional physical forms:
  • Also available in electronic format.
Contents:
1. Introduction -- 2. Sparse recovery : problem formulations -- 3. Theoretical results (deterministic part) -- 4. Theoretical results (probabilistic part) -- 5. Algorithms for sparse recovery problems -- 6. Beyond LASSO : structured sparsity -- 7. Beyond LASSO : other loss functions -- 8. Sparse graphical models -- 9. Sparse matrix factorization : dictionary learning and beyond.
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"A Chapman & Hall book."

Includes bibliographical references (pages 203-226) and index.

1. Introduction -- 2. Sparse recovery : problem formulations -- 3. Theoretical results (deterministic part) -- 4. Theoretical results (probabilistic part) -- 5. Algorithms for sparse recovery problems -- 6. Beyond LASSO : structured sparsity -- 7. Beyond LASSO : other loss functions -- 8. Sparse graphical models -- 9. Sparse matrix factorization : dictionary learning and beyond.

Also available in electronic format.

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