A sequential multilinear Nystr\"om algorithm for streaming low-rank approximation of tensors in Tucker format
<|reference_start|>A sequential multilinear Nystr\"om algorithm for streaming low-rank approximation of tensors in Tucker format: 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.<|reference_end|>
@article{bucci2024a,
    title={A sequential multilinear Nystr\"om algorithm for streaming low-rank
  approximation of tensors in Tucker format},
    author={Alberto Bucci, Behnam Hashemi},
    journal={arXiv preprint arXiv:2407.03849},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03849},
    primaryClass={math.NA cs.NA}
}