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Biclustering via Mixtures of Regression Models

  • Raja VeluEmail author
  • Zhaoque Zhou
  • Chyng Wen Tee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

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.

Keywords

Multivariate regression Singular value decomposition Dimension reduction Mixture models 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Whitman School of ManagementSyracuse UniversitySyracuseUSA
  2. 2.Lee Kong Chian School of BusinessSingapore Management UniversitySingaporeSingapore

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