Biclustering via Sparse Singular Value Decomposition | Academic Article individual record
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.

author list (cited authors)
Lee, M., Shen, H., Huang, J. Z., & Marron, J. S.
publication date
2010
publisher
Wiley Publisher
published in
BIOMETRICS Journal
keywords
  • Biclustering
  • Databases, Factual
  • Nutritional Physiological Phenomena
  • Linear Models
  • High-dimension Low Sample Size
  • Cluster Analysis
  • Lung Neoplasms
  • Principal Component Analysis
  • Dimension Reduction
  • Algorithms
  • Penalization
  • Adaptive Lasso
  • Humans
citation count

135

PubMed Central ID
20163403
identifier
128611SE
Digital Object Identifier (DOI)
start page
1087
end page
1095
volume
66
issue
4