Diffusion Maps for Signal Filtering in Graph Learning
Todd Hildebrant
This paper explores the application diffusion maps as graph shift operators
in understanding the underlying geometry of graph signals. The study evaluates
the improvements in graph learning when using diffusion map generated filters
to the Markov Variation minimization problem. The paper showcases the
effectiveness of this approach through examples involving synthetically
generated and real-world temperature sensor data. These examples also compare
the diffusion map graph signal model with other commonly used graph signal
operators. The results provide new approaches for the analysis and
understanding of complex, non-Euclidean data structures.