Greedy Approaches to Symmetric Orthogonal Tensor Decomposition

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.

To appear in SIAM Journal on Matrix Analysis and Applications (SIMAX)