On Accelerating the Regularized Alternating Least Square Algorithm for Tensors
Numerical Analysis
2017-07-25 v2
Abstract
In this paper, we discuss the acceleration of the regularized alternating least square (RALS) algorithm for tensor approximation. We propose a fast iterative method using a Aitken-Stefensen like updates for the regularized algorithm. Through numerical experiments, the fast algorithm demonstrate a faster convergence rate for the accelerated version in comparison to both the standard and regularized alternating least squares algorithms. In addition, we analyze the global convergence based on the Kurdyka- Lojasiewicz inequality as well as show that the RALS algorithm has a linear local convergence rate.
Keywords
Cite
@article{arxiv.1507.04721,
title = {On Accelerating the Regularized Alternating Least Square Algorithm for Tensors},
author = {Xiaofei Wang and Carmeliza Navasca and Stefan Kindermann},
journal= {arXiv preprint arXiv:1507.04721},
year = {2017}
}