A new linear dimensionality reduction technique based on chernoff distance

Authors:
Luis Rueda;Myriam Herrera
Affiliations:
Department of Computer Science and Center for Biotechnology, University of Concepción, Concepción, Chile;Department and Institute of Informatics, National University of San Juan, San Juan, Argentina
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

A new linear dimensionality reduction (LDR) technique for pattern classification and machine learning is presented, which, though linear, aims at maximizing the Chernoff distance in the transformed space. The corresponding two-class criterion, which is maximized via a gradient-based algorithm, is presented and initialization procedures are also discussed. Empirical results of this and traditional LDR approaches combined with two well-known classifiers, linear and quadratic, on synthetic and real-life data show that the proposed criterion outperforms the traditional schemes.