Practical Applications of Sparse Modeling (Neural Information Processing)

Rish, Irina [Editor]; Cecchi, Guillermo A. [Editor]; Lozano, Aurelie [Editor]; Niculescu-Mizil, Alexandru [Editor]; Rish, Irina [Contributor]; Cecchi, Guillermo A. [Contributor]; Lozano, Aurelie [Contributor]; Niculescu-Mizil, Alexandru [Contributor]; Meyer, Pablo [Contributor]; Rosset, Saharon [Contributor]; Xing, Eric P. [Contributor]; Kolar, Mladen [Contributor]; Kim, Seyoung [Contributor]; Chen, Xi [Contributor]; Slawski, Martin [Contributor]; Hein, Matthias [Contributor]; Strother, Stephen C. [Contributor]; Rasmussen, Peter M. [Contributor]; Churchill, Nathan W. [Contributor]; Hansen, Lars Kai [Contributor]; Carroll, Melissa K. [Contributor]; Garg, Rahul [Contributor]; Baliki, Marwan [Contributor]; Apkarian, A. Vania [Contributor]; Mal

ISBN 10: 0262027720 / ISBN 13: 9780262027724

Published by Mit Pr, 2014

About this Item

LIKE NEW/UNREAD!!! Text is Clean and Unmarked! Has a small black line or red dot on bottom/exterior edge of pages.

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Bibliographic Details

• Title: Practical Applications of Sparse Modeling (Neural Information Processing)
• Publisher: Mit Pr
• Publication Date: 2014
• Language: English
• ISBN 10: 0262027720
• ISBN 13: 9780262027724
• Binding: hardcover
• Condition: As New
• Seller catalogs:
  • 1

About this title

Synopsis

Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision.

Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision.

Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models.

Contributors
A. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, Rémi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Seunghak Lee, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing

About the Author

Irina Rish, Guillermo Cecchi, and Aurelie Lozao are Research Staff Members at IBM T. J. Watson Research Center, New York. Alexandru Niculescu-Mizil is a Researcher at the Machine Learning Department at NEC Labs America, Princeton, New Jersey.