Online Reweighted Least Squares Robust PCA

The letter deals with the problem known as robust principal component analysis (RPCA), that is, the decomposition of a data matrix as the sum of a low-rank matrix component and a sparse matrix component. After expressing the low-rank matrix component in factorized form, we develop a novel online RPCA algorithm that is based entirely on reweighted least squares recursions and is appropriate for sequential data processing. The proposed algorithm is fast, memory optimal and, as corroborated by indicative empirical results on simulated data and a video processing application, competitive to the state-of-the-art in terms of estimation performance.

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