Rosipal, R; Girolami, M; Trejo, LJ; (2000) Kernel PCA feature extraction of event-related potentials for human signal detection performance. In: Malmgren, H and Borga, M and Niklasson, L, (eds.) ARTIFICIAL NEURAL NETWORKS IN MEDICINE AND BIOLOGY. (pp. 321 - 326). SPRINGER-VERLAG LONDON LTD
Full text not available from this repository.
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.
|Title:||Kernel PCA feature extraction of event-related potentials for human signal detection performance|
|Event:||Conference on Artificial Neural Networks in Medicine and Biology (ANNIMAB-1)|
|Dates:||2000-05-13 - 2000-05-16|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
Archive Staff Only: edit this record