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)


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.


Multivariate regression Singular value decomposition Dimension reduction Mixture models 


  1. 1.
    Chen, L., Huang, J.Z.: Sparse reduced-rank regression for simultaneous dimension reduction and variable selection. J. Am. Stat. 107(500), 1533–1545 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Frühwirth-Schnatter, S.: Finite Mixture and Markov Switching Models. Springer, New York (2006). Scholar
  3. 3.
    Hasbrouck, J., Seppi, D.J.: Common factors in prices, order flows, and liquidity. J. Financ. Econ. 59(3), 383–411 (2001)CrossRefGoogle Scholar
  4. 4.
    Lee, M., Shen, H., Huang, J.Z., Marron, J.S.: Biclustering via sparse singular value decomposition. Biometrics 66(4), 1087–1095 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Qin, L.-X., Self, S.G.: The clustering of regression models method with applications in gene expression data. Biometrics 62(2), 526–533 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Rao, C.R.: The use and interpretation of principal component analysis in applied research. JSankhyā: Indian J. Stat. Ser. A 26, 329–358 (1964)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Reinsel, G.C., Velu, R.: Multivariate Reduced-Rank Regression: Theory and Applications. Springer, New York (1998). Scholar
  8. 8.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 68(1), 49–67 (2006)MathSciNetCrossRefGoogle Scholar

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

Personalised recommendations