CPD Updating Using Low-Rank Weights
Michiel Vandecappelle, Martijn Bousse, Nico Vervliet, Lieven De Lathauwer
Abstract
Tensor updating methods enable tensor decompositions to adapt quickly when new data is added to the tensor. At present, updating methods for the canonical polyadic decomposition (CPD) give every tensor entry the same weight. In practice, however, data quality or relative importance might differ between tensor entries, which warrants the use of more general weighting schemes. In this paper, an NLS updating method is developed for the CPD that uses a weighted least squares (WLS) approach with a low-rank weight tensor. This weight tensor itself can also be updated to allow dynamic weighting schemes. By exploiting the CPD structure of both the data and weight tensors, the algorithm obtains better accuracy than the unweighted updating methods, while being more time- and memory efficient than batch WLS methods.
BibTeX
@inproceedings{icassp2018_cpdupdatingusing,
title = {CPD Updating Using Low-Rank Weights},
author = {Michiel Vandecappelle and Martijn Bousse and Nico Vervliet and Lieven De Lathauwer},
booktitle = {ICASSP 2018},
year = {2018}
}