Landmark Alternating Diffusion
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910428912680960 |
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| author | Yeh, Sing-Yuan Wu, Hau-Tieng Talmon, Ronen Tsui, Mao-Pei |
| author_facet | Yeh, Sing-Yuan Wu, Hau-Tieng Talmon, Ronen Tsui, Mao-Pei |
| contents | Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remains a limitation. Inspired by the landmark diffusion idea considered in the Robust and Scalable Embedding via Landmark Diffusion (ROSELAND), we propose a variation of AD, called Landmark AD (LAD), which captures the essence of AD while offering superior computational efficiency. We provide a series of theoretical analyses of LAD under the manifold setup and apply it to the automatic sleep stage annotation problem with two electroencephalogram channels to demonstrate its application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Landmark Alternating Diffusion Yeh, Sing-Yuan Wu, Hau-Tieng Talmon, Ronen Tsui, Mao-Pei Machine Learning Statistics Theory Data Analysis, Statistics and Probability 53Z50, 65D18 Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remains a limitation. Inspired by the landmark diffusion idea considered in the Robust and Scalable Embedding via Landmark Diffusion (ROSELAND), we propose a variation of AD, called Landmark AD (LAD), which captures the essence of AD while offering superior computational efficiency. We provide a series of theoretical analyses of LAD under the manifold setup and apply it to the automatic sleep stage annotation problem with two electroencephalogram channels to demonstrate its application. |
| title | Landmark Alternating Diffusion |
| topic | Machine Learning Statistics Theory Data Analysis, Statistics and Probability 53Z50, 65D18 |
| url | https://arxiv.org/abs/2404.19649 |