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
EditorsJack J. Dongarra, João M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot
PublisherSpringer Verlag
Pages533-549
Number of pages17
ISBN (Print)9783030227401
DOIs
StatePublished - Jan 1 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

Fingerprint

Biclustering
Singular value decomposition
Regression Model
Finance
Lasso
Predictors
Coefficient
Model

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Velu, R., Zhou, Z., & Tee, C. W. (2019). Biclustering via Mixtures of Regression Models. In J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, ... P. M. A. Sloot (Eds.), Computational Science – ICCS 2019 - 19th International Conference, Proceedings (pp. 533-549). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11537 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22741-8_38

Biclustering via Mixtures of Regression Models. / Velu, Raja; Zhou, Zhaoque; Tee, Chyng Wen.

Computational Science – ICCS 2019 - 19th International Conference, Proceedings. ed. / Jack J. Dongarra; João M.F. Rodrigues; Pedro J.S. Cardoso; Jânio Monteiro; Roberto Lam; Valeria V. Krzhizhanovskaya; Michael H. Lees; Peter M.A. Sloot. Springer Verlag, 2019. p. 533-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11537 LNCS).

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

Velu, R, Zhou, Z & Tee, CW 2019, Biclustering via Mixtures of Regression Models. in JJ Dongarra, JMF Rodrigues, PJS Cardoso, J Monteiro, R Lam, VV Krzhizhanovskaya, MH Lees & PMA Sloot (eds), Computational Science – ICCS 2019 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11537 LNCS, Springer Verlag, pp. 533-549, 19th International Conference on Computational Science, ICCS 2019, Faro, Portugal, 6/12/19. https://doi.org/10.1007/978-3-030-22741-8_38
Velu R, Zhou Z, Tee CW. Biclustering via Mixtures of Regression Models. In Dongarra JJ, Rodrigues JMF, Cardoso PJS, Monteiro J, Lam R, Krzhizhanovskaya VV, Lees MH, Sloot PMA, editors, Computational Science – ICCS 2019 - 19th International Conference, Proceedings. Springer Verlag. 2019. p. 533-549. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-22741-8_38
Velu, Raja ; Zhou, Zhaoque ; Tee, Chyng Wen. / Biclustering via Mixtures of Regression Models. Computational Science – ICCS 2019 - 19th International Conference, Proceedings. editor / Jack J. Dongarra ; João M.F. Rodrigues ; Pedro J.S. Cardoso ; Jânio Monteiro ; Roberto Lam ; Valeria V. Krzhizhanovskaya ; Michael H. Lees ; Peter M.A. Sloot. Springer Verlag, 2019. pp. 533-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@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}",
year = "2019",
month = "1",
day = "1",
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 = "Dongarra, {Jack J.} and 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.}",
booktitle = "Computational Science – ICCS 2019 - 19th International Conference, Proceedings",

}

TY - GEN

T1 - Biclustering via Mixtures of Regression Models

AU - Velu, Raja

AU - Zhou, Zhaoque

AU - Tee, Chyng Wen

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Dimension reduction

KW - Mixture models

KW - Multivariate regression

KW - Singular value decomposition

UR - http://www.scopus.com/inward/record.url?scp=85067684133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067684133&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22741-8_38

DO - 10.1007/978-3-030-22741-8_38

M3 - Conference contribution

SN - 9783030227401

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 533

EP - 549

BT - Computational Science – ICCS 2019 - 19th International Conference, Proceedings

A2 - Dongarra, Jack J.

A2 - Rodrigues, João M.F.

A2 - Cardoso, Pedro J.S.

A2 - Monteiro, Jânio

A2 - Lam, Roberto

A2 - Krzhizhanovskaya, Valeria V.

A2 - Lees, Michael H.

A2 - Sloot, Peter M.A.

PB - Springer Verlag

ER -