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Robust principal component analysis (RPCA) has drawn significant attentionsdue to its powerful capability in recovering low-rank matrices as well assuccessful appplications in various real world problems. The currentstate-of-the-art algorithms usually need to solve singular value decompositionof large matrices, which generally has at least a quadratic or even cubiccomplexity. This drawback has limited the application of RPCA in solving realworld problems. To combat this drawback, in this paper we propose a new type ofRPCA method, RES-PCA, which is linearly efficient and scalable in both datasize and dimension. For comparison purpose, AltProj, an existing scalableapproach to RPCA requires the precise knowlwdge of the true rank; otherwise, itmay fail to recover low-rank matrices. By contrast, our method works with orwithout knowing the true rank; even when both methods work, our method isfaster. Extensive experiments have been performed and testified to theeffectiveness of proposed method quantitatively and in visual quality, whichsuggests that our method is suitable to be employed as a light-weight, scalablecomponent for RPCA in any application pipelines.

RES-PCA: A Scalable Approach to Recovering Low-rank Matrices