Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals

• URL: http://arxiv.org/abs/2310.19005v2
• Date: Tue, 7 Nov 2023 11:12:31 GMT
• Title: Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals
• Authors: Mohamad H. Alizade, Aref Einizade, and Jhony H. Giraldo
• Abstract summary: We introduce Kernel-based joint Multiple GL and clustering of graph signals applications. Experiments demonstrate that KMGL significantly enhances the robustness of GL clustering, particularly in scenarios with high noise levels. These findings underscore the potential of KMGL for improving the performance of Graph Signal Processing methods in diverse real-world applications.
• Score: 2.4305626489408465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations. However, real-world data often contains diverse information, necessitating the simultaneous clustering and learning of multiple graphs. In practical applications, valuable node-specific covariates, represented as kernels, have been underutilized by existing graph signal clustering methods. In this letter, we propose a new framework, named Kernel-based joint Multiple GL and clustering of graph signals (KMGL), that leverages a multi-convex optimization approach. This allows us to integrate node-side information, construct low-pass filters, and efficiently solve the optimization problem. The experiments demonstrate that KMGL significantly enhances the robustness of GL and clustering, particularly in scenarios with high noise levels and a substantial number of clusters. These findings underscore the potential of KMGL for improving the performance of GSP methods in diverse, real-world applications.

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