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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© 2000 Springer-Verlag London
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Rosipal, R., Girolami, M., Trejo, L.J. (2000). Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_49
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_49
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
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