Greedy Approaches to Symmetric Orthogonal Tensor Decomposition
Numerical Analysis
2017-06-06 v1
Abstract
Finding the symmetric and orthogonal decomposition (SOD) of a tensor is a recurring problem in signal processing, machine learning and statistics. In this paper, we review, establish and compare the perturbation bounds for two natural types of incremental rank-one approximation approaches. Numerical experiments and open questions are also presented and discussed.
Cite
@article{arxiv.1706.01169,
title = {Greedy Approaches to Symmetric Orthogonal Tensor Decomposition},
author = {Cun Mu and Daniel Hsu and Donald Goldfarb},
journal= {arXiv preprint arXiv:1706.01169},
year = {2017}
}
Comments
To appear in SIAM Journal on Matrix Analysis and Applications (SIMAX)