An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Algorithms for the Sample Mean of Graphs
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Graph classification by means of Lipschitz embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Journal of Machine Learning Research
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Generalized learning graph quantization
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Generalized learning graph quantization
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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We show that extending the Gaussian distribution to the domain of graphs corresponds to truncated Gaussian distributions in Euclidean spaces. Based on this observation, we derive a maximum likelihood method for estimating the parameters of the Gaussian on graphs. In conjunction with a naive Bayes classifier, we applied the proposed approach to image classification.