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- Brian Lois, Namrata Vaswani
- 2015 IEEE International Symposium on Information…
- 2015

“To be considered for an 2015 IEEE Jack Keil Wolf ISIT Student Paper Award.” This work studies two interrelated problems - online robust PCA (RPCA) and online matrix completion (MC). Both problems assume that an accurate estimate of the low-dimensional subspace from which the first true data vector is generated is available. We develop a… (More)

- Brian Lois, Namrata Vaswani
- 2015 IEEE International Conference on Acoustics…
- 2015

We study the problem of sequentially recovering a sparse vector x<sub>t</sub> and a vector from a low-dimensional subspace ℓ<sub>t</sub> from knowledge of their sum m<sub>t</sub> = x<sub>t</sub> + ℓ<sub>t</sub>. If the primary goal is to recover the low-dimensional subspace where the ℓ<sub>t</sub>'s lie, then the problem is one of… (More)

- Brian Lois, Namrata Vaswani, Chenlu Qiu
- 2013 IEEE Global Conference on Signal and…
- 2013

We study the problem of recursively reconstructing a time sequence of sparse vectors S<sub>t</sub> from measurements of the form M<sub>t</sub> = AS<sub>t</sub> +BL<sub>t</sub> where A and B are known measurement matrices, and L<sub>t</sub> lies in a slowly changing low dimensional subspace. We assume that the signal of interest (S<sub>t</sub>) is sparse,… (More)

- Jinchun Zhan, Brian Lois, Namrata Vaswani
- AISTATS
- 2016

In this work we develop and study a novel online robust principal components' analysis (RPCA) algorithm based on the recently introduced ReProCS framework. Our algorithm significantly improves upon the original ReProCS algorithm and it also returns even more accurate offline estimates. The key contribution of this work is a correct-ness result for this… (More)

—We study the problem of recursively reconstructing a time sequence of sparse vectors St from measurements of the form Mt = ASt + BLt where A and B are known measurement matrices, and Lt lies in a slowly changing low dimensional subspace. We assume that the signal of interest (St) is sparse, and has support which is correlated over time. We introduce a… (More)

- Chenlu Qiu, Namrata Vaswani, Brian Lois, Leslie Hogben
- ArXiv
- 2013

This work studies the recursive robust principal components analysis (PCA) problem. If the outlier is the signal-of-interest, this problem can be interpreted as one of recursively recovering a time sequence of sparse vectors, $S_t$, in the presence of large but structured noise, $L_t$. The structure that we assume on $L_t$ is that $L_t$ is dense and lies in… (More)

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