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arXiv · 2407.03849

A sequential multilinear Nystr\"om algorithm for streaming low-rank approximation of tensors in Tucker format

Alberto Bucci·Behnam Hashemi
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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 $\mathcal{A}$ exclusively through random sketches of the original data, the algorithm effectively leverages structures in $\mathcal{A}$, 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.

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Alberto Bucci, Behnam Hashemi. 2024-07-04. A sequential multilinear Nystr\"om algorithm for streaming low-rank approximation of tensors in Tucker format. https://arxiv.org/abs/2407.03849

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