A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
An energy function and continuous edit process for graph matching
Neural Computation
Replicator equations, maximal cliques, and graph isomorphism
Neural Computation
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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In this paper we address the problem of comparing and classifying protein surfaces through a kernelized version of the Softassign graph-matching algorithm. Preliminary experiments with random-generated graphs have suggested that weighting the quadratic cost function of Softassign with information coming from the computation of diffusion kernels on graphs attenuate the performance decay with increasing noise levels. Our experimental results show that this approach yields a useful similarity measure to cluster proteins with similar structure, to automatically find prototypical graphs representing families of proteins and also to classify proteins in terms of their distance to these prototypes. We also show that the role of kernel-based information is to smooth the obtained matching fields, which in turn results in noise-free prototype estimation.