• Corpus ID: 1939560

Nonconvex Relaxation Approaches to Robust Matrix Recovery

@inproceedings{Wang2013NonconvexRA,
  title={Nonconvex Relaxation Approaches to Robust Matrix Recovery},
  author={Shusen Wang and Dehua Liu and Zhihua Zhang},
  booktitle={IJCAI},
  year={2013}
}
Motivated by the recent developments of nonconvex penalties in sparsity modeling, we propose a nonconvex optimization model for handing the low-rank matrix recovery problem. Different from the famous robust principal component analysis (RPCA), we suggest recovering low-rank and sparse matrices via a nonconvex loss function and a nonconvex penalty. The advantage of the nonconvex approach lies in its stronger robustness. To solve the model, we devise a majorization-minimization augmented Lagrange… 

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