Sparse and Low-Rank Tensor Decomposition

Annual Conference on Neural Information Processing Systems, 2015.

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Abstract:

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' algorithm for tensor factorization. Our method relies on a reduction of the problem to sparse and low-rank matrix decomposition vi...More

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