Randomized Online CP Decomposition
Abstract
CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.
Keywords
Cite
@article{arxiv.2007.10798,
title = {Randomized Online CP Decomposition},
author = {Congbo Ma and Xiaowei Yang and Hu Wang},
journal= {arXiv preprint arXiv:2007.10798},
year = {2020}
}