Emergent Mind
Sketching for low-rank nonnegative matrix approximation: Numerical study
(2201.11154)
Published Jan 26, 2022
in
math.NA
,
cs.NA
,
and
math.OC
Abstract
We propose new approximate alternating projection methods, based on randomized sketching, for the low-rank nonnegative matrix approximation problem: find a low-rank approximation of a nonnegative matrix that is nonnegative, but whose factors can be arbitrary. We calculate the computational complexities of the proposed methods and evaluate their performance in numerical experiments. The comparison with the known deterministic alternating projection methods shows that the randomized approaches are faster and exhibit similar convergence properties.
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