@inproceedings{ed795bc2ea6d4994bac764fb5afc5768,
title = "Biclustering via Mixtures of Regression Models",
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 = "Dimension reduction, Mixture models, Multivariate regression, Singular value decomposition",
author = "Raja Velu and Zhaoque Zhou and Tee, {Chyng Wen}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 19th International Conference on Computational Science, ICCS 2019 ; Conference date: 12-06-2019 Through 14-06-2019",
year = "2019",
doi = "10.1007/978-3-030-22741-8_38",
language = "English (US)",
isbn = "9783030227401",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "533--549",
editor = "Rodrigues, {Jo{\~a}o M.F.} and Cardoso, {Pedro J.S.} and J{\^a}nio Monteiro and Roberto Lam and Krzhizhanovskaya, {Valeria V.} and Lees, {Michael H.} and Sloot, {Peter M.A.} and Dongarra, {Jack J.}",
booktitle = "Computational Science – ICCS 2019 - 19th International Conference, Proceedings",
}