Heat kernel coupling for multiple graph analysis
Computer Vision and Pattern Recognition
2013-12-12 v1
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.
Cite
@article{arxiv.1312.3035,
title = {Heat kernel coupling for multiple graph analysis},
author = {Michael M. Bronstein and Klaus Glashoff},
journal= {arXiv preprint arXiv:1312.3035},
year = {2013}
}