publication . Conference object . Preprint . 2017

efficient low rank tensor ring completion

Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin;
Open Access
  • Published: 23 Jul 2017
  • Publisher: IEEE
Abstract
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation. This development is motivated in part by the success of matrix completion algorithms that alternate over the (low-rank) factors. In this paper, we propose a spectral initialization for the tensor ring completion algorithm and analyze the computational complexity of the proposed algorithm. We numerically compare it with existing methods that employ a low rank tensor train approximation for data compl...
Subjects
ACM Computing Classification System: MathematicsofComputing_NUMERICALANALYSIS
free text keywords: Artificial intelligence, business.industry, business, Pattern recognition, Algorithm, Matrix product state, Matrix decomposition, Matrix completion, Initialization, Tensor, Computational complexity theory, Algorithm design, Computer science, Approximation algorithm, Computer Science - Learning, Computer Science - Information Theory
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publication . Conference object . Preprint . 2017

efficient low rank tensor ring completion

Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin;