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Classification of dissimilarity data with a new flexible Mahalanobis-like metric

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Abstract

Statistical pattern recognition traditionally relies on feature-based representation. For many applications, such vector representation is not available and we only possess proximity data (distance, dissimilarity, similarity, ranks, etc.). In this paper, we consider a particular point of view on discriminant analysis from dissimilarity data. Our approach is inspired by the Gaussian classifier and we defined decision rules to mimic the behavior of a linear or a quadratic classifier. The number of parameters is limited (two per class). Numerical experiments on artificial and real data show interesting behavior compared to Support Vector Machines and to kNN classifier: (a) lower or equivalent error rate, (b) equivalent CPU time, (c) more robustness with sparse dissimilarity data.

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Acknowledgments

The authors would like to sincerely thank Gilles Celeux, who inspired this work and for all the fruitful and valuable scientific discussions, which allowed this study to be carried out.

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Authors and Affiliations

  1. Laboratory of Audio and Video, Technical University of Sofia, Sofia, Bulgaria

    Agata Manolova

  2. Department of Images and Signals, GIPSA-Lab, INPG-CNRS, Grenoble, France

    Agata Manolova & Anne Guérin-Dugué

Authors
  1. Agata Manolova
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  2. Anne Guérin-Dugué
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Corresponding author

Correspondence to Agata Manolova.

Additional information

This work is supported by grants of the “Fonds National pour la Science”, from the program “ACI Masse de Données” and the project “DataHighDim”.

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Manolova, A., Guérin-Dugué, A. Classification of dissimilarity data with a new flexible Mahalanobis-like metric. Pattern Anal Applic 11, 337–351 (2008). https://doi.org/10.1007/s10044-008-0101-6

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