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Roman Rosipal Mark Girolami Leonard J. Trejo Andrzej Cichocki 《Neural computing & applications》2001,10(3):231-243
In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection
in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed
in the regression problems of chaotic Mackey–Glass time-series prediction in a noisy environment and estimating human signal
detection performance from brain event-related potentials elicited by task relevant signals. We compared results obtained
using either Kernel PCA or linear PCA as data preprocessing steps. On the human signal detection task, we report the superiority
of Kernel PCA feature extraction over linear PCA. Similar to linear PCA, we demonstrate de-noising of the original data by
the appropriate selection of various nonlinear principal components. The theoretical relation and experimental comparison
of Kernel Principal Components Regression, Kernel Ridge Regression and ε-insensitive Support Vector Regression is also provided. 相似文献