Greedy Approaches to Symmetric Orthogonal Tensor Decomposition

06/05/2017
by   Cun Mu, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/29/2017

Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors

Many idealized problems in signal processing, machine learning and stati...
research
03/12/2021

Rank properties and computational methods for orthogonal tensor decompositions

The orthogonal decomposition factorizes a tensor into a sum of an orthog...
research
07/25/2022

Approximate Real Symmetric Tensor Rank

We investigate the effect of an ε-room of perturbation tolerance on symm...
research
10/08/2020

Orthogonal Decomposition of Tensor Trains

In this paper we study the problem of recovering a tensor network decomp...
research
06/14/2019

Optimal orthogonal approximations to symmetric tensors cannot always be chosen symmetric

We study the problem of finding orthogonal low-rank approximations of sy...
research
08/06/2020

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration

In this paper, we develop novel perturbation bounds for the high-order o...
research
12/05/2020

Approximation Algorithms for Sparse Best Rank-1 Approximation to Higher-Order Tensors

Sparse tensor best rank-1 approximation (BR1Approx), which is a sparsity...

Please sign up or login with your details

Forgot password? Click here to reset