Kernel PCA for feature extraction and de-noising in nonlinear regression.
NEURAL COMPUT APPL
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 epsilon-insensitive Support Vector Regression is also provided.
|Title:||Kernel PCA for feature extraction and de-noising in nonlinear regression|
|Keywords:||de-noising, feature extraction, human performance monitoring, kernel functions, nonlinear regression, principal components, EVENT-RELATED POTENTIALS, PERFORMANCE|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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