Pattern Recognition Using a Nonlinear PCA
نویسندگان
چکیده
Principal Component Analysis (PCA) has been applied for the feature extraction of high-dimensional data in pattern recognition. However, PCA does can not extract nonlinear characteristics of the datadistribution appropriately. In order to solve this problem, we have proposed a method of nonlinear PCA (NLPCA) which preserves the order of the principal components and we have implemented the NLPCA with neural networks. In this paper, we propose a novel method of pattern recognition using the NLPCA which can extract a nonlinear eigenspace from the data-distribution. We examine its e ectiveness through some experiments.
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