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Tuesday, November 20, 2012

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. 


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.

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