The dissimilarity space: Bridging structural and statistical pattern recognition
Pattern Recognition Letters
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
One-sided prototype selection on class imbalanced dissimilarity matrices
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Dissimilarities can be a powerful way to represent objects like strings, graphs and images for which it is difficult to find good features. The resulting dissimilarity space may be used to train any classifier appropriate for feature spaces. There is, however, a strong need for dimension reduction. Straightforward procedures for prototype selection as well as feature selection have been used for this in the past. Complicated sets of objects may need more advanced procedures to overcome local minima. In this paper it is shown that genetic algorithms, previously used for feature selection, may be used for building good dissimilarity spaces as well, especially when small sets of prototypes are needed for computational reasons.