Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917561376964608 |
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| author | Wu, Hau-Tieng |
| author_facet | Wu, Hau-Tieng |
| contents | In this manuscript, we propose an efficient manifold denoiser based on landmark diffusion and optimal shrinkage under the complicated high dimensional noise and compact manifold setup. It is flexible to handle several setups, including the high ambient space dimension with a manifold embedding that occupies a subspace of high or low dimensions, and the noise could be colored and dependent. A systematic comparison with other existing algorithms on both simulated and real datasets is provided. This manuscript is mainly algorithmic and we report several existing tools and numerical results. Theoretical guarantees and more comparisons will be reported in the official paper of this manuscript. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising Wu, Hau-Tieng Machine Learning Applications In this manuscript, we propose an efficient manifold denoiser based on landmark diffusion and optimal shrinkage under the complicated high dimensional noise and compact manifold setup. It is flexible to handle several setups, including the high ambient space dimension with a manifold embedding that occupies a subspace of high or low dimensions, and the noise could be colored and dependent. A systematic comparison with other existing algorithms on both simulated and real datasets is provided. This manuscript is mainly algorithmic and we report several existing tools and numerical results. Theoretical guarantees and more comparisons will be reported in the official paper of this manuscript. |
| title | Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2401.03921 |