Biclustering via Sparse Singular Value Decomposition
Article first published online: 16 FEB 2010
© 2010, The International Biometric Society
Volume 66, Issue 4, pages 1087–1095, December 2010
How to Cite
Lee, M., Shen, H., Huang, J. Z. and Marron, J. S. (2010), Biclustering via Sparse Singular Value Decomposition. Biometrics, 66: 1087–1095. doi: 10.1111/j.1541-0420.2010.01392.x
- Issue published online: 16 FEB 2010
- Article first published online: 16 FEB 2010
- Received December 2008. Revised December 2009. Accepted December 2009.
- Adaptive lasso;
- Dimension reduction;
- High-dimension low sample size;
- Principal component analysis
Summary 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.