Fast Randomized Algorithms for Low-Rank Matrix Approximations with Applications in Comparative Analysis of Genome-Scale Expression Data Sets

This paper proposes a randomized algorithm to efficiently compute the generalized singular value decomposition (GSVD) of two data matrices, with applications in comparative analysis of genome-scale expression data sets.