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Sparse and Low-Rank Tensor Decomposition
Parikshit Shah · Nikhil Rao · Gongguo Tang

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #82

Motivated by the problem of robust factorization of a low-rank tensor, we study the question of sparse and low-rank tensor decomposition. We present an efficient computational algorithm that modifies Leurgans' algoirthm for tensor factorization. Our method relies on a reduction of the problem to sparse and low-rank matrix decomposition via the notion of tensor contraction. We use well-understood convex techniques for solving the reduced matrix sub-problem which then allows us to perform the full decomposition of the tensor. We delineate situations where the problem is recoverable and provide theoretical guarantees for our algorithm. We validate our algorithm with numerical experiments.

Author Information

Parikshit Shah (Yahoo Labs)
Nikhil Rao (University of Texas at Austin)
Gongguo Tang (Colorado School of Mines)

Gongguo Tang is an Assistant Professor in the Department of Electrical Engineering at Colorado School of Mines since 2014. Before that, he was a visiting scholar at Simons Institute for the Theory of Computing at University of California, Berkeley in Fall 2013 and a postdoc working with Professor Robert Nowak at the University of Wisconsin-Madison and Professor Benjamin Recht at the University of California, Berkeley from August 2011 to December 2013. He received his Ph.D. in Electrical Engineering from Washington University in St. Louis under the supervision of Professor Arye Nehorai.

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