
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.


coremsg

fast-randomized-algorithms-for-low-rank-matrix-approximations-with-applications-in-comparative-analysis-of-genome-scale-expression-data-sets


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