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This paper presented a new unsupervised learning method to find a set of templates specific to the objects. Kernel PCA, as an unsupervised learning method, is a nonlinear extension of PCA for finding projections that give useful nonlinear descriptors of the data, which gave the system improved performance with continued use by adjusting the clusters, and by creating a new cluster whenever an unusual...
This paper reviewed the classical principal components analysis methods for multivariate data analysis and feature extraction in pattern classification. A kernel-based extension to the classical PCA models was discussed to cope with nonlinear data dependencies. Kernel PCA was implicitly performing a linear PCA in some high-dimensional kernel feature space that was nonlinearly related to input space...
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