Adaptive Cross Tubal Tensor Approximation
Numerical Analysis
2023-05-12 v2 Numerical Analysis
Abstract
In this paper, we propose a new adaptive cross algorithm for computing a low tubal rank approximation of third-order tensors, with less memory and lower computational complexity than the truncated tensor SVD (t-SVD). This makes it applicable for decomposing large-scale tensors. We conduct numerical experiments on synthetic and real-world datasets to confirm the efficiency and feasibility of the proposed algorithm. The simulation results show more than one order of magnitude acceleration in the computation of low tubal rank (t-SVD) for large-scale tensors. An application to pedestrian attribute recognition is also presented.
Cite
@article{arxiv.2305.05030,
title = {Adaptive Cross Tubal Tensor Approximation},
author = {Salman Ahmadi-Asl and Anh Huy Phan and Andrzej Cichocki and Anastasia Sozykina and Zaher Al Aghbari and Jun Wang and Ivan Oseledets},
journal= {arXiv preprint arXiv:2305.05030},
year = {2023}
}