An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
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High efficiency and quality: large graphs matching
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MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
The dissimilarity representation for structural pattern recognition
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High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
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In graph comparison, the use of (dis)similarity measurements between graphs is an important topic. In this work, we propose an eigendecomposition based approach for measuring dissimilarities between graphs in the joint eigenspace (JoEig). We will compare our JoEig approach with two other eigendecomposition based methods that compare graphs in different eigenspaces. To calculate the dissimilarity between graphs of different sizes and perform inexact graph comparison, we further develop three different ways to resize the eigenspectra and study their performance in different situations.