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arXiv · 2404.19649

Landmark Alternating Diffusion

Sing-Yuan Yeh·Hau-Tieng Wu·Ronen Talmon·Mao-Pei Tsui
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Abstract

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.

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BibTeXRIS

Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon, Mao-Pei Tsui. 2024-04-29. Landmark Alternating Diffusion. https://arxiv.org/abs/2404.19649

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