A Unified Framework for Stochastic Matrix Factorization via Variance Reduction

Renbo Zhao; William B. Haskell; Jiashi Feng

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arXiv · 1705.06884

A Unified Framework for Stochastic Matrix Factorization via Variance Reduction

Renbo Zhao·William B. Haskell·Jiashi Feng

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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 $ε$-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.

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Renbo Zhao, William B. Haskell, Jiashi Feng. 2017-05-22. A Unified Framework for Stochastic Matrix Factorization via Variance Reduction. https://arxiv.org/abs/1705.06884

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