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Title: Tensor Completion with Nearly Linear Samples Given Weak Side Information
Tensor completion exhibits an interesting computational-statistical gap in terms of the number of samples needed to perform tensor estimation. While there are only Θ(tn) degrees of freedom in a t-order tensor with n^t entries, the best known polynomial time algorithm requires O(n^t/2 ) samples in order to guarantee consistent estimation. In this paper, we show that weak side information is sufficient to reduce the sample complexity to O(n). The side information consists of a weight vector for each of the modes which is not orthogonal to any of the latent factors along that mode; this is significantly weaker than assuming noisy knowledge of the subspaces. We provide an algorithm that utilizes this side information to produce a consistent estimator with O(n^1+κ ) samples for any small constant κ > 0. We also provide experiments on both synthetic and real-world datasets that validate our theoretical insights.  more » « less
Award ID(s):
1948256 1955997
PAR ID:
10332706
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the ACM on Measurement and Analysis of Computing Systems
Volume:
6
Issue:
2
ISSN:
2476-1249
Page Range / eLocation ID:
1 to 35
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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