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Article: Biclustering via Sparse Singular Value Decomposition

TitleBiclustering via Sparse Singular Value Decomposition
Authors
KeywordsPrincipal component analysis
Adaptive lasso
Biclustering
Dimension reduction
High-dimension low sample size
Penalization
Issue Date2010
Citation
Biometrics, 2010, v. 66, n. 4, p. 1087-1095 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/219639
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906

 

DC FieldValueLanguage
dc.contributor.authorLee, Mihee-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHuang, Jianhua Z.-
dc.contributor.authorMarron, J. S.-
dc.date.accessioned2015-09-23T02:57:35Z-
dc.date.available2015-09-23T02:57:35Z-
dc.date.issued2010-
dc.identifier.citationBiometrics, 2010, v. 66, n. 4, p. 1087-1095-
dc.identifier.issn0006-341X-
dc.identifier.urihttp://hdl.handle.net/10722/219639-
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.-
dc.languageeng-
dc.relation.ispartofBiometrics-
dc.subjectPrincipal component analysis-
dc.subjectAdaptive lasso-
dc.subjectBiclustering-
dc.subjectDimension reduction-
dc.subjectHigh-dimension low sample size-
dc.subjectPenalization-
dc.titleBiclustering via Sparse Singular Value Decomposition-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1541-0420.2010.01392.x-
dc.identifier.pmid20163403-
dc.identifier.scopuseid_2-s2.0-78650059908-
dc.identifier.volume66-
dc.identifier.issue4-
dc.identifier.spage1087-
dc.identifier.epage1095-
dc.identifier.eissn1541-0420-

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