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Can feature information interaction help for information fusion in multimedia problems?

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Abstract

This article presents the information-theoretic based feature information interaction, a measure that can describe complex feature dependencies in multivariate settings. According to the theoretical development, feature interactions are more accurate than current, bivariate dependence measures due to their stable and unambiguous definition. In experiments with artificial and real data we compare first the empirical dependency estimates of correlation, mutual information and 3-way feature interaction. Then, we present feature selection and classification experiments that show superior performance of interactions over bivariate dependence measures for the artificial data, for real world data this goal is not achieved yet.

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Acknowledgement

This work is funded by the European project MultiMATCH (EU-IST-STREP#033104).

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Authors and Affiliations

  1. CUI, University of Geneva, 7 route de Drize, 1227, Carouge, Switzerland

    Jana Kludas, Eric Bruno & Stéphane Marchand-Maillet

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  1. Jana Kludas
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  2. Eric Bruno
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  3. Stéphane Marchand-Maillet
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Correspondence to Jana Kludas.

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Kludas, J., Bruno, E. & Marchand-Maillet, S. Can feature information interaction help for information fusion in multimedia problems?. Multimed Tools Appl 42, 57–71 (2009). https://doi.org/10.1007/s11042-008-0251-y

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