Abstract
In this paper, we propose the application of the Kernel PCA technique for feature selection in high-dimensional feature space where input
variables are mapped by a Gaussian kernel. The extracted features are employed
in the regression problem of estimating human signal detection performance
from brain event-related potentials elicited by task relevant signals. We
report the superiority of Kernel PCA for feature extraction over linear PCA.
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