pith. sign in

arxiv: 1503.01375 · v1 · pith:4CDTX2TDnew · submitted 2015-03-04 · 🧮 math.NA · cs.NA

Symmetric Orthogonal Tensor Decomposition is Trivial

classification 🧮 math.NA cs.NA
keywords symmetricorthogonaltensordecompositionmethodproblemsreal-valuedanandkumar
0
0 comments X
read the original abstract

We consider the problem of decomposing a real-valued symmetric tensor as the sum of outer products of real-valued, pairwise orthogonal vectors. Such decompositions do not generally exist, but we show that some symmetric tensor decomposition problems can be converted to orthogonal problems following the whitening procedure proposed by Anandkumar et al. (2012). If an orthogonal decomposition of an $m$-way $n$-dimensional symmetric tensor exists, we propose a novel method to compute it that reduces to an $n \times n$ symmetric matrix eigenproblem. We provide numerical results demonstrating the effectiveness of the method.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks

    cs.LG 2019-06 unverdicted novelty 6.0

    Presents an active-sampling method that approximates the weight subspace from Hessian finite differences, recovers the rank-1 tensors by robust nonlinear programming, and attributes layers with gradient descent, yield...