Nested AdaBoost procedure for classification and multi-class nonlinear discriminant analysis

AdaBoost methods find an accurate classifier by combining moderate learners that can be computed using traditional techniques based, for instance, on separating hyperplanes. Recently, we proposed a strategy to compute each moderate learner using a linear ensemble of weak classifiers that are built through the kernel support vector machine (KSVM) hypersurface geometry. In this way, we apply 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 decision rule. In this paper, we explore this methodology in two ways: (a) For classification in principal component analysis (PCA) spaces; (b) For multi-class nonlinear discriminant PCA, named MNDPCA. Up to the best of our knowledge, the former is a new AdaBoost-based classification technique. Besides, in this paper we study the influence of kernel types for MNDPCA in order to set a near optimum configuration for feature selection and ranking in PCA subspaces. We compare the proposed methodologies with counterpart ones using facial expressions of the Radboud Faces database and Karolinska Directed Emotional Faces (KDEF) image database. Our experimental results have shown that MNDPCA outperforms counterpart techniques for selecting PCA features in the Radboud database while it performs close to the best technique for KDEF images. Moreover, the proposed classifier achieves outstanding recognition rates if compared with the literature techniques.