Fast and Accurate Tensor Completion with Tensor Trains: A System Identification Approach

04/17/2018 ∙ by Ching-Yun Ko, et al. ∙ 0

We propose a novel tensor completion approach by equating it to a system identification task. The key is to regard the coordinates and values of the known entries as inputs and outputs, respectively. By assuming a tensor train format initialized with low-rank tensor cores, the latter are iteratively identified via a simple alternating linear scheme to reduce residuals. Experiments verify the superiority of the proposed scheme in terms of both speed and accuracy, where a speedup of up to 23× is observed compared to state-of-the-art tensor completion methods at a similar accuracy.



There are no comments yet.


page 5

page 6

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.