Diffusion Maps for Signal Filtering in Graph Learning
This work addresses graph signal processing for analyzing non-Euclidean data, but it appears incremental as it applies an existing method (diffusion maps) to a known problem (graph learning).
The paper tackled the problem of graph learning by applying diffusion maps as graph shift operators to understand graph signal geometry, evaluating improvements in Markov Variation minimization with synthetic and real-world temperature sensor data. The results provided new approaches for analyzing complex, non-Euclidean data structures, though no concrete numbers were reported.
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.