Paper Info
Title
A Sampling-Based Method for Tensor Ring Decomposition
Abstract
We propose a sampling-based method for computing the tensor ring (TR) decomposition of a data tensor. The method uses leverage score sampled alternating least squares to fit the TR cores in an iterative fashion. By taking advantage of the special structure of TR tensors, we can efficiently estimate the leverage scores and attain a method which has complexity sublinear in the number of input tensor entries. We provide high-probability relative-error guarantees for the sampled least squares problems. We compare our proposal to existing methods in experiments on both synthetic and real data. Our method achieves substantial speedup-sometimes two or three orders of magnitude-over competing methods, while maintaining good accuracy. We also provide an example of how our method can be used for rapid feature extraction.
Year
Venue
DocType
2021
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
Conference
Volume
ISSN
Citations 
139
2640-3498
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Osman Asif Malik121.74
Stephen R. Becker243627.61