  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.
