Algebraic Methods for Tensor Data
Representation Theory
2021-01-19 v1 Numerical Analysis
Combinatorics
Numerical Analysis
Abstract
We develop algebraic methods for computations with tensor data. We give 3 applications: extracting features that are invariant under the orthogonal symmetries in each of the modes, approximation of the tensor spectral norm, and amplification of low rank tensor structure. We introduce colored Brauer diagrams, which are used for algebraic computations and in analyzing their computational complexity. We present numerical experiments whose results show that the performance of the alternating least square algorithm for the low rank approximation of tensors can be improved using tensor amplification.
Cite
@article{arxiv.2005.12988,
title = {Algebraic Methods for Tensor Data},
author = {Neriman Tokcan and Jonathan Gryak and Kayvan Najarian and Harm Derksen},
journal= {arXiv preprint arXiv:2005.12988},
year = {2021}
}