• Corpus ID: 17515447

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

@article{Wang2017AUV,
title={A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery},
author={Lingxiao Wang and Xiao Zhang and Quanquan Gu},
journal={arXiv: Machine Learning},
year={2017}
}
• Published 9 January 2017
• Computer Science
• arXiv: Machine Learning
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. Based upon the mild restricted strong convexity and smoothness conditions, we derive a projected notion of the restricted Lipschitz continuous…

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