Title: A Unified Framework for Stochastic Matrix Factorization via Variance Reduction,
Abstract: We propose a unified framework to speed up the existing stochastic matrix
factorization (SMF) algorithms via variance reduction. Our framework is general
and it subsumes several well-known SMF formulations in the literature. We
perform a non-asymptotic convergence analysis of our framework and derive
computational and sample complexities for our algorithm to converge to an
$\epsilon$-stationary point in expectation. In addition, extensive experiments
for a wide class of SMF formulations demonstrate that our framework
consistently yields faster convergence and a more accurate output dictionary
vis-à-vis state-of-the-art frameworks.