Practical Applications of Sparse Modeling

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  • Practical Applications of Sparse Modeling Book Detail

  • Author : Irina Rish
  • Release Date : 2014-09-12
  • Publisher : MIT Press
  • Genre : Computers
  • Pages : 265
  • ISBN 13 : 0262027720
  • File Size : 45,45 MB

Practical Applications of Sparse Modeling by Irina Rish PDF Summary

Book Description: "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 data sets. This collection describes key approaches in sparse modeling, focusing on its applications in such fields as 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"--Jacket.

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Practical Applications of Sparse Modeling

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

Sparse Modeling

Sparse Modeling

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Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a p