A Nonlinear Subspace Method for Pattern Recognition using a Nonlinear PCA

R. Saegusa and S. Hashimoto (Japan)


Subspace method, Pattern Recognition, Feature Extraction, Nonlinear PCA, Neural Networks, Eigenspace


Linear subspace method based on principal component analysis has been applied for pattern recognition of high dimensional data. However, the linear subspace method can not represent nonlinear characteristics of the data distribution efficiently. In order to overcome this prob lem, some nonlinear subspace methods have been pro posed. In this paper, we propose a novel nonlinear sub space method for pattern recognition using multi-layered perceptrons which can hierarchically construct a nonlinear subspace from the data-distribution. We introduce the op timization scheme of the subspace taking into account the classification. The proposed method achieves high classi fication accuracy by the optimization scheme and provides the finite subspace to avoid the crossover of the subspaces. We examine its effectiveness through some experiments.

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