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

ICML 2017 David AndersonMing Gu

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 factorization called Spectrum-Revealing LU (SRLU) for effective low-rank matrix approximation, and develop a fast algorithm to compute an SRLU factorization... (read more)

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