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
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Central Clustering of Attributed Graphs
Machine Learning
Median graphs: A genetic approach based on new theoretical properties
Pattern Recognition
Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Multiple alignment of contact maps
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Consistent Estimator of Median and Mean Graph
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Maximum likelihood method for parameter estimation of bell-shaped functions on graphs
Pattern Recognition Letters
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One challenge in bridging the gap between structural and statistical pattern recognition consists in studying combinatorial structures like graphs using probabilistic methods. This contribution presents the structural counterparts of the first and second fundamental theorem in probability, (1) the law of large numbers and (2) the central limit theorem. In addition, we derive characterizations and uniqueness conditions for the mean of graphs. As a special case, we investigate the weighted mean of two graphs. The proposed results establish a sound statistical foundation for unsupervised structural pattern recognition methods.