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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1087-1095 |
| Number of pages | 9 |
| Journal | Biometrics |
| Volume | 66 |
| Issue number | 4 |
| DOIs | |
| State | Published - Feb 16 2010 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): CA57030, KUS-CI-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
