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    Biclustering via Sparse Singular Value Decomposition

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    Type
    Article
    Authors
    Lee, Mihee
    Shen, Haipeng
    Huang, Jianhua Z.
    Marron, J. S.
    KAUST Grant Number
    CA57030
    KUS-CI-016-04
    Date
    2010-02-16
    Online Publication Date
    2010-02-16
    Print Publication Date
    2010-12
    Permanent link to this record
    http://hdl.handle.net/10754/597666
    
    Metadata
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    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.
    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
    Wiley
    Journal
    Biometrics
    DOI
    10.1111/j.1541-0420.2010.01392.x
    PubMed ID
    20163403
    ae974a485f413a2113503eed53cd6c53
    10.1111/j.1541-0420.2010.01392.x
    Scopus Count
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