An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation

David G. Anderson, Ming Gu

Abstract: Low-rank matrix approximation is a fundamental tool in data analysis for processing large datasets, reducing noise, and finding important signals. In this work, we present a novel truncated LU fact...