Strongly Convex Programming for Principal Component Pursuit
نویسندگان
چکیده
In this paper, we address strongly convex programming for principal component pursuit with reduced linear measurements, which decomposes a superposition of a low-rank matrix and a sparse matrix from a small set of linear measurements. We first provide sufficient conditions under which the strongly convex models lead to the exact low-rank and sparse matrix recovery; Second, we also give suggestions on how to choose suitable parameters in practical algorithms.
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ورودعنوان ژورنال:
- CoRR
دوره abs/1209.4405 شماره
صفحات -
تاریخ انتشار 2012