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2008 19th International Conference on Pattern Recognition

DOI: 10.1109/icpr.2008.4761286

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Feature Condensing Algorithm for Feature Selection

Proceedings article published in 2008 by Pavel Křížek, Josef Kittler, Václav Hlaváč ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

A new unsupervised filter-based feature selection method is introduced. Its principle consists in merging similar features into clusters using a distance measure derived from the correlation coefficient. Subsequently, only one representative feature is selected from each cluster. In experiments with real-world data, we show that the proposed method is benefical as a pre-filtering step for more sophisticated feature selection techniques.