Biclustering via Mixtures of Regression Models

Raja Velu, Zhaoque Zhou, Chyng Wen Tee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biclustering of observations and the variables is of interest in many scientific disciplines; In a single set of data matrix it is handled through the singular value decomposition. Here we deal with two sets of variables: Response and predictor sets. We model the joint relationship via regression models and then apply SVD on the coefficient matrix. The sparseness condition is introduced via Group Lasso; the approach discussed here is quite general and is illustrated with an example from Finance.

Original languageEnglish (US)
Title of host publicationComputational Science – ICCS 2019 - 19th International Conference, Proceedings
EditorsJoão M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot, Jack J. Dongarra
PublisherSpringer Verlag
Pages533-549
Number of pages17
ISBN (Print)9783030227401
DOIs
StatePublished - 2019
Event19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal
Duration: Jun 12 2019Jun 14 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11537 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Computational Science, ICCS 2019
CountryPortugal
CityFaro
Period6/12/196/14/19

Keywords

  • Dimension reduction
  • Mixture models
  • Multivariate regression
  • Singular value decomposition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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