Scalable Robust Matrix Recovery: Frank--Wolfe Meets Proximal Methods
نویسندگان
چکیده
منابع مشابه
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
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...
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ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2016
ISSN: 1064-8275,1095-7197
DOI: 10.1137/15m101628x