B.: A kernel autoassociator approach to pattern classification

Abstract—Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the recon-struction problem, using linear and multivariate polynomial func-tions, respectively. We apply the proposed model to novelty detec-tion with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to class classification problems including wine recog-nition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains. Index Terms—Kernel machine, nonlinear associative memory, pattern recognition. I