Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods

  title={Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods},
  author={Cun Mu and Yuqian Zhang and John Wright and Donald Goldfarb},
  journal={SIAM J. Scientific Computing},
Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learning. In theory, under certain conditions, this problem can be solved in polynomial time via a natural convex relaxation, known as Compressive Principal Component Pursuit (CPCP). However, many existing provably convergent algorithms for CPCP su↵er from superlinear per-iteration cost, which severely limits their… CONTINUE READING
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