Heat kernel coupling for multiple graph analysis

Abstract

In this paper, we introduce heat kernel coupling (HKC) as a method of constructing multimodal spectral geometry on weighted graphs of different size without vertex-wise bijective correspondence. We show that Laplacian averaging can be derived as a limit case of HKC, and demonstrate its applications on several problems from the manifold learning and pattern recognition domain.

References in corpus (4)

• Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds
• Learning Multi-modal Similarity
• Multimodal diffusion geometry by joint diagonalization of Laplacians
• Making Laplacians commute

Cited by in corpus (1)

• Generalized Spectral Clustering via Gromov-Wasserstein Learning