A new approach to multi-class linear dimensionality reduction

Authors:
Luis Rueda;Myriam Herrera
Affiliations:
Department of Computer Science and Center for Biotecnology, University of Concepción, Concepción, Chile;Department and Institute of Informatics, National University of San Juan, San Juan, Argentina
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

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Abstract

Linear dimensionality reduction (LDR) is quite important in pattern recognition due to its efficiency and low computational complexity. In this paper, we extend the two-class Chernoff-based LDR method to deal with multiple classes. We introduce the criterion, as well as the algorithm that maximizes such a criterion. The proof of convergence of the algorithm and a formal procedure to initialize the parameters of the algorithm are also given. We present empirical simulations on standard well-known multi-class datasets drawn from the UCI machine learning repository. The results show that the proposed LDR coupled with a quadratic classifier outperforms the traditional LDR schemes.