Novel multiclass classifiers based on the minimization of the within-class variance

Kotsia, Irene and Pitas, Ioannis and Zafeiriou, Stefanos (2009) Novel multiclass classifiers based on the minimization of the within-class variance. IEEE Transactions on Neural Networks, 20 (1). pp. 14-34. ISSN 1045-9227

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Official URL: http://dx.doi.org/10.1109/TNN.2008.2004376

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Abstract

In this paper, a novel class of multiclass classifiers inspired by the optimization of Fisher discriminant ratio and the support vector machine (SVM) formulation is introduced. The optimization problem of the so-called minimum within-class variance multiclass classifiers (MWCVMC) is formulated and solved in arbitrary Hilbert spaces, defined by Mercer's kernels, in order to find multiclass decision hyperplanes/surfaces. Afterwards, MWCVMCs are solved using indefinite kernels and dissimilarity measures via pseudo-Euclidean embedding. The power of the proposed approach is first demonstrated in the facial expression recognition of the seven basic facial expressions (i.e., anger, disgust, fear, happiness, sadness, and surprise plus the neutral state) problem in the presence of partial facial occlusion by using a pseudo-Euclidean embedding of Hausdorff distances and the MWCVMC. The experiments indicated a recognition accuracy rate achieved up to 99%. The MWCVMC classifiers are also applied to face recognition and other classification problems using Mercer's kernels.

Item Type:Article
Keywords (uncontrolled):Face recognition, Fisher linear discriminant analysis (FLDA), Mercer's kernels, facial expression recognition, multiclass classifiers, pseudo-Euclidean embedding, support vector machines (SVMs)
Research Areas:School of Science and Technology > Science & Technology
ID Code:9550
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Deposited On:19 Nov 2012 07:04
Last Modified:04 Jun 2013 14:28

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