A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery

9 Jan 2017Lingxiao WangXiao ZhangQuanquan Gu

We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery. Starting from an appropriate initial estimator, our proposed algorithm performs projected gradient descent based on a novel semi-stochastic gradient specifically designed for low-rank matrix recovery... (read more)

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