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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2409.09138 |
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| _version_ | 1866909548432850944 |
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| author | Dash, Anirudh Siripuram, Aditya |
| author_facet | Dash, Anirudh Siripuram, Aditya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Structured Orthogonal Dictionary Learning using Householder Reflections Dash, Anirudh Siripuram, Aditya Signal Processing Machine Learning 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. |
| title | Fast Structured Orthogonal Dictionary Learning using Householder Reflections |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2409.09138 |