ReProCS: Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise - implementation -

We've talked about ReProCS before but here iit looks like the analysis is more complete. I also like the fact that we can address the fact that noise lies in a small dimensional manifold. 

Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise by Chenlu Qiu, Namrata Vaswani, Leslie Hogben. The abstract reads:

This work studies the recursive robust principal components' analysis(PCA) problem. Here, "robust" refers to robustness to both independent and correlated sparse outliers. If the outlier is the signal-of-interest, this problem can be interpreted as one of recursively recovering a time sequence of sparse vectors, St, in the presence of large but structured noise, Lt. The structure that we assume on Lt is that Lt is dense and lies in a low dimensional subspace that is either fixed or changes "slowly enough". A key application where this problem occurs is in video surveillance where the goal is to separate a slowly changing background (Lt) from moving foreground objects (St) on-the-fly. To solve the above problem, we introduce a novel solution called Recursive Projected CS (ReProCS). Under mild assumptions, we show that, with high probability (w.h.p.), ReProCS can exactly recover the support set of St at all times; and the reconstruction errors of both St and Lt are upper bounded by a time-invariant and small value at all times. We also show how the algorithm and its guarantees extend to the undersampled measurements' case.

ReProCS is available here.


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