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Practical applications of sparse modeling

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"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"--MIT CogNet.OCLC-licensed vendor bibliographic record.

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Detail Information

Series Title

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Call Number

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Publisher

: The MIT Press., 2014

Collation

1 online resource (xii, 249 pages).

Language

English

ISBN/ISSN

9780262325325

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Sampling (Statistics)
Mathematical models.
Data reduction.
Sparse matrices.

Specific Detail Info

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Statement of Responsibility

edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil.

Other Information

Cataloger

Rudy k

Source

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Validator

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Digital Object Identifier (DOI)

https://direct.mit.edu/books/edited-volume/4027/Practical-Applications-of-Sparse-Modeling

Journal Volume

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Journal Issue

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