Type
ArticleKAUST Grant Number
CA57030KUS-CI-016-04
Date
2010-02-16Online Publication Date
2010-02-16Print Publication Date
2010-12Permanent link to this record
http://hdl.handle.net/10754/597666
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Show full item recordAbstract
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.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).Publisher
WileyJournal
BiometricsPubMed ID
20163403ae974a485f413a2113503eed53cd6c53
10.1111/j.1541-0420.2010.01392.x
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