Biclustering via Mixtures of Regression Models

Raja Velu, Zhaoque Zhou, Chyng Wen Tee

Research output: Chapter in Book/Entry/PoemConference contribution


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
Number of pages17
ISBN (Print)9783030227401
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


Conference19th International Conference on Computational Science, ICCS 2019


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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