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In this study, Kernel Principal Component Analysis (KPCA) is applied as feature selection in a high-dimensional feature space which is nonlinearly related to an input space. By using Mercer Kernels, w...e can compute principal components in a high dimensional feature space. Then, the extracted features by KPCA method are employed as a new kind of regressors in an ordinary least square regression in the feature space which is Reproducing Kernel Hilbert Space (RKHS). In the experiment, KPCR method was applied to predict the Mackey-Glass time series.続きを見る
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