A sequential multilinear Nystr\"om algorithm for streaming low-rank approximation of tensors in Tucker format
Numerical Analysis
2024-08-22 v2 Numerical Analysis
Abstract
We present a sequential version of the multilinear Nystr\"om algorithm which is suitable for the low-rank Tucker approximation of tensors given in a streaming format. Accessing the tensor exclusively through random sketches of the original data, the algorithm effectively leverages structures in , such as low-rankness, and linear combinations. We present a deterministic analysis of the algorithm and demonstrate its superior speed and efficiency in numerical experiments including an application in video processing.
Cite
@article{arxiv.2407.03849,
title = {A sequential multilinear Nystr\"om algorithm for streaming low-rank approximation of tensors in Tucker format},
author = {Alberto Bucci and Behnam Hashemi},
journal= {arXiv preprint arXiv:2407.03849},
year = {2024}
}