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Award ID contains: 1739736

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  1. (, IEEE Transactions on Information Theory)
    Full Text Available 
  2. ; (, Electronic Journal of Statistics)
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  3. ; (, Journal of machine learning research)
    Robust PCA is a widely used statistical procedure to recover an underlying low-rank matrix with grossly corrupted observations. This work considers the problem of robust PCA as a nonconvex optimization problem on the manifold of low-rank matrices and proposes two algorithms based on manifold optimization. It is shown that, with a properly designed initialization, the proposed algorithms are guaranteed to converge to the underlying lowrank matrix linearly. Compared with a previous work based on the factorization of low-rank matrices Yi et al. (2016), the proposed algorithms reduce the dependence on the condition number of the underlying low-rank matrix theoretically. Simulations and real data examples con rm the competitive performance of our method. 
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    Accepted Manuscript Full Text Available