Fast Structured Orthogonal Dictionary Learning using Householder Reflections
<|reference_start|>Fast Structured Orthogonal Dictionary Learning using Householder Reflections: In this paper, we propose and investigate algorithms for the structured orthogonal dictionary learning problem. First, we investigate the case when the dictionary is a Householder matrix. We give sample complexity results and show theoretically guaranteed approximate recovery (in the $l_{\infty}$ sense) with optimal computational complexity. We then attempt to generalize these techniques when the dictionary is a product of a few Householder matrices. We numerically validate these techniques in the sample-limited setting to show performance similar to or better than existing techniques while having much improved computational complexity.<|reference_end|>
@article{dash2024fast,
    title={Fast Structured Orthogonal Dictionary Learning using Householder
  Reflections},
    author={Anirudh Dash, Aditya Siripuram},
    journal={arXiv preprint arXiv:2409.09138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09138},
    primaryClass={eess.SP cs.LG}
}