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.
Cite
@article{arxiv.2404.19649,
title = {Landmark Alternating Diffusion},
author = {Sing-Yuan Yeh and Hau-Tieng Wu and Ronen Talmon and Mao-Pei Tsui},
journal= {arXiv preprint arXiv:2404.19649},
year = {2024}
}