Handle URI:
http://hdl.handle.net/10754/597666
Title:
Biclustering via Sparse Singular Value Decomposition
Authors:
Lee, Mihee; Shen, Haipeng; Huang, Jianhua Z.; Marron, J. S.
Abstract:
Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.
Citation:
Lee M, Shen H, Huang JZ, Marron JS (2010) Biclustering via Sparse Singular Value Decomposition. Biometrics 66: 1087–1095. Available: http://dx.doi.org/10.1111/j.1541-0420.2010.01392.x.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
CA57030; KUS-CI-016-04
Issue Date:
16-Feb-2010
DOI:
10.1111/j.1541-0420.2010.01392.x
PubMed ID:
20163403
Type:
Article
ISSN:
0006-341X
Sponsors:
The authors extend grateful thanks to one coeditor, one associate editor, and three reviewers for their constructive comments. ML, HS, and JSM are partially supported by NSF grant DMS-0606577. JZH is partially supported by NSF grants DMS-0606580, DMS-0907170, NCI grant CA57030, and award KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorLee, Miheeen
dc.contributor.authorShen, Haipengen
dc.contributor.authorHuang, Jianhua Z.en
dc.contributor.authorMarron, J. S.en
dc.date.accessioned2016-02-25T12:44:01Zen
dc.date.available2016-02-25T12:44:01Zen
dc.date.issued2010-02-16en
dc.identifier.citationLee M, Shen H, Huang JZ, Marron JS (2010) Biclustering via Sparse Singular Value Decomposition. Biometrics 66: 1087–1095. Available: http://dx.doi.org/10.1111/j.1541-0420.2010.01392.x.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid20163403en
dc.identifier.doi10.1111/j.1541-0420.2010.01392.xen
dc.identifier.urihttp://hdl.handle.net/10754/597666en
dc.description.abstractSparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.en
dc.description.sponsorshipThe authors extend grateful thanks to one coeditor, one associate editor, and three reviewers for their constructive comments. ML, HS, and JSM are partially supported by NSF grant DMS-0606577. JZH is partially supported by NSF grants DMS-0606580, DMS-0907170, NCI grant CA57030, and award KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherWiley-Blackwellen
dc.subjectAdaptive lassoen
dc.subjectBiclusteringen
dc.subjectDimension reductionen
dc.subjectHigh-dimension low sample sizeen
dc.subjectPenalizationen
dc.subjectPrincipal component analysisen
dc.titleBiclustering via Sparse Singular Value Decompositionen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.contributor.institutionThe University of North Carolina at Chapel Hill, Chapel Hill, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberCA57030en
kaust.grant.numberKUS-CI-016-04en

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