Landmark Alternating Diffusion
<|reference_start|>Landmark Alternating Diffusion: 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.<|reference_end|>
@article{yeh2024landmark,
    title={Landmark Alternating Diffusion},
    author={Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon, Mao-Pei Tsui},
    journal={arXiv preprint arXiv:2404.19649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19649},
    primaryClass={cs.LG math.ST physics.data-an stat.ML stat.TH}
}