Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2005.843980
Title: A kernel autoassociator approach to pattern classification
Authors: Zhang, H.
Huang, W.
Huang, Z. 
Zhang, B.
Keywords: Kernel machine
Nonlinear associative memory
Pattern recognition
Issue Date: 2005
Source: Zhang, H., Huang, W., Huang, Z., Zhang, B. (2005). A kernel autoassociator approach to pattern classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35 (3) : 593-606. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2005.843980
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 nonlinear autoassociation models emphasize searching for the nonlinear 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 reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, 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. © 2005 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/38960
ISSN: 10834419
DOI: 10.1109/TSMCB.2005.843980
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