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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Rueda, L., & Herrera, M. (2006). A new linear dimensionality reduction technique based on chernoff distance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4140 LNAI, pp. 299–308). Springer Verlag. https://doi.org/10.1007/11874850_34
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