Research summary and some important papers of Namrata Vaswani

Research Summary (new - updated Dec8 2014)

Talk on Recursive Structured Signals Recovery and Applications in Bio-Imaging, ECE dept colloquium at UIUC, December 2013

Talk on Online Robust PCA at UT Austin, Rice and TAMU, November 2014

Some key papers:

o    A Correctness Result for Online Robust PCA, submitted, on arXiv:1409.3959 [cs.IT].

o    Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise, IEEE Trans. Information Theory, August 2014

o    An Online Algorithm for Separating Sparse and Low-dimensional Signal Sequences from their Sum, IEEE Trans. Signal Processing, August 2014, (older title: Practical ReProCS for Separating Sparse and Low-dimensional Signal Sequences from their Sum)

  Introduced a novel online robust principal components' analysis (PCA) solution called Recursive Projected CS (ReProCS) that uses two extra but usually practically valid assumptions beyond those used by the batch methods it needs accurate initial subspace knowledge and slow subspace change.

  Online algorithms are needed for real-time applications; and even for offline applications, they are faster and need less storage compared to batch techniques. Moreover, online approaches can provide a natural way to exploit temporal dependencies in the dataset. We can show that ReProCS uses slow subspace change to provably allow for significantly more correlated support sets of the sparse vectors compared to some batch methods.

  Application to video analytics problems was demonstrated we showed that ReProCS significantly outperforms most existing batch and online robust PCA algorithms for separating difficult video sequences into foreground and background layers. See webpages of Han Guo or Chenlu Qiu

  Obtained a correctness result for ReProCS. We show that under mild assumptions, with high probability, ReProCS can exactly recover the support set of the sparse vectors at all times; and the reconstruction errors for both the sparse and low-dimensional vectors are bounded by a time-invariant and small value. Moreover, the subspace recovery error decays down to a small value within a short delay after a subspace change. To the best of our knowledge, our result is the first correctness result for an online (recursive) robust PCA algorithm or equivalently for sparse plus low-rank matrix recovery.

All publications

Dynamic MRI reconstruction