1. Landmark Alternating Diffusion
- Author
-
Yeh, Sing-Yuan, Wu, Hau-Tieng, Talmon, Ronen, and Tsui, Mao-Pei
- Subjects
Computer Science - Machine Learning ,Mathematics - Statistics Theory ,Physics - Data Analysis, Statistics and Probability ,Statistics - Machine Learning ,53Z50, 65D18 - 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.
- Published
- 2024