Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph embedding for pattern recognition
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Graph matching based on dot product representation of graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
High efficiency and quality: large graphs matching
Proceedings of the 20th ACM international conference on Information and knowledge management
Graph embedding using commute time
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Recent results on heat kernel embedding of graphs
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Graph matching based on spectral embedding with missing value
Pattern Recognition
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient geometric graph matching using vertex embedding
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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This paper describes how graph-spectral methods can be used to transform the node correspondence problem into one of point-set alignment. We commence by using the ISOMAP algorithm to embed the nodes of a graph in a low-dimensional Euclidean space. With the nodes in the graph transformed to points in a metric space, we can recast the problem of graph-matching into that of aligning the points. Here we use a variant of the Scott and Longuet-Higgins algorithm to find point correspondences. We experiment with the resulting algorithmon a number of real-world problems.