RCUR: an R package for CUR matrix decomposition.

ABSTRACT:

Background

Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD). Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed

SUBMITTER: Bodor A 

PROVIDER: S-EPMC3546429 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature