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
Self-organizing maps
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Soft learning vector quantization
Neural Computation
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
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
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
Maximum likelihood for gaussians on graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Maximum likelihood for gaussians on graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
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This contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (lgq) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform lgq and lgq2.1.