Multi-Class Nonlinear Discriminant Feature Analysis

Tiene Andre Filisbino¹; Gilson antonio Giraldi¹; Carlos Eduardo Thomaz²

¹ Laboratório Nacional de Computação Científica; ² Centro Universitário da FEI

doi:10.20906/CPS/CILAMCE2017-0392

Resumo

The problem of ranking features in N-class problems have been addressed by the multi-class discriminant principal component analysis (MDPCA) for texture and face image classification. In this paper we present a nonlinear version of the MDPCA, named multi-class nonlinear discriminant feature analysis (MNDFA), that is based on kernel support vector machines (KSVM) and AdaBoost techniques. Specifically, the problem of ranking features, computed from multi-class databases, is addressed by applying the AdaBoost procedure in a nested loop: each iteration of the inner loop boosts weak classifiers to a moderate one while the outer loop combines the moderate classifiers to build the global discriminant vector. The inner and outer loop procedures use AdaBoost techniques to combine learners. In the proposed MNDFA, each weak learner is a linear classifier computed through a separating hyperplane, defined by a KSVM decision boundary, in the feature space. In the computational experiments we analyse the obtained approach using a five-class granite image database. Our experimental results have shown that the features selected by the proposed technique allow competitive recognition rates when compared with related methods.

Palavras-chave: Nonlinear; Multi-Class; KSVM; Discriminant Analysis; AdaBoost; Texture Analysis; Classification