Making Tensor Factorizations Robust to Non-Gaussian Noise
Abstract
Tensors are multi-way arrays, and the Candecomp/Parafac (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective function, which is a maximum likelihood estimate under the assumption of i.i.d. Gaussian noise. We demonstrate that this loss function can actually be highly sensitive to non-Gaussian noise. Therefore, we propose a loss function based on the 1-norm because it can accommodate both Gaussian and grossly non-Gaussian perturbations. We also present an alternating majorization-minimization algorithm for fitting a CP model using our proposed loss function.
Cite
@article{arxiv.1010.3043,
title = {Making Tensor Factorizations Robust to Non-Gaussian Noise},
author = {Eric C. Chi and Tamara G. Kolda},
journal= {arXiv preprint arXiv:1010.3043},
year = {2010}
}
Comments
Contributed presentation at the NIPS Workshop on Tensors, Kernels, and Machine Learning, Whistler, BC, Canada, December 10, 2010