Multi-Penalty Regularization for Detecting Relevant Variables.

Multi-Penalty Regularization for Detecting Relevant Variables.

Abstract

In this paper we propose a new method for detecting relevant variables from a priori given high-dimensional data under the assumption that input-output dependence is described by a nonlinear function depending on a few variables. The method is based on the inspection of the behavior of discrepancies of a multi-penalty regularization with a component-wise penalization for small and large values of regularization parameters. We provide the justification of the proposed method under a certain condition on sampling operators. The effectiveness of the method is demonstrated in the example with synthetic data and in the reconstruction of gene regulatory networks. In the latter example, the obtained results provide a clear evidence of the competitiveness of the proposed method.

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Authors
  • Schindlerova, Katerina
  • Naumova, Valeriya
  • Pereverzyev Jr., Sergiy
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Editors
  • Pesenson, I.
  • Le Gia, Q.T.
  • Mayeli, A.
  • Mhaskar, H.
  • Zhou, D.-X.
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  • Springer International Publishing AG
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Shortfacts
Category
Book Section/Chapter
Divisions
Data Mining
Subjects
Informatik Allgemeines
Title of Book
Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science
ISSN/ISBN
2296-5009
Page Range
pp. 889-916
Date
20 June 2017
Official URL
http://www.springer.com/gp/book/9783319555553
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