Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
This addresses the scalability bottleneck in robust matrix recovery for applications in computer vision and machine learning, offering an incremental improvement over existing methods.
The paper tackled the problem of recovering matrices from compressive and grossly corrupted observations, which is fundamental in robust statistics, by proposing a scalable method that combines Frank-Wolfe and proximal techniques to achieve linear per-iteration cost, as demonstrated in numerical experiments on visual data.
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, all existing provable algorithms for CPCP suffer from superlinear per-iteration cost, which severely limits their applicability to large scale problems. In this paper, we propose provable, scalable and efficient methods to solve CPCP with (essentially) linear per-iteration cost. Our method combines classical ideas from Frank-Wolfe and proximal methods. In each iteration, we mainly exploit Frank-Wolfe to update the low-rank component with rank-one SVD and exploit the proximal step for the sparse term. Convergence results and implementation details are also discussed. We demonstrate the scalability of the proposed approach with promising numerical experiments on visual data.