2012/09/20 by You, Qingshan, Wan, Qun, Liu, Yipeng
#FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Numerical Analysis (math.NA)
paper · doi:10.48550/arxiv.1209.4405
In this paper, we address strongly convex programming for princi- pal 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 recov- ery; Second, we also give suggestions on how to choose suitable parameters in practical algorithms.