Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor

Alexander Gutierrez; Jarvis Haupt; Swayambhoo Jain

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Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor

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arxiv 1704.02534 v1 pith:U6ME37BG submitted 2017-04-08 stat.ML

Swayambhoo Jain , Alexander Gutierrez , Jarvis Haupt This is my paper

classification stat.ML

keywords boundscanonicalcompletioncomplexity-regularizedlikelihoodmaximumnoisypolyadic

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In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with one of the factors being sparse. We present general theoretical error bounds for an estimate obtained by using a complexity-regularized maximum likelihood principle and then instantiate these bounds for the case of additive white Gaussian noise. We also provide an ADMM-type algorithm for solving the complexity-regularized maximum likelihood problem and validate the theoretical finding via experiments on synthetic data set.

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