NDLI: Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image

Content Provider	IEEE Xplore Digital Library
Author	Kallas, M. Honeine, P. Richard, C. Amoud, H. Francis, C.
Copyright Year	2010
Description	Author affiliation: Institut Charles Delaunay (UMR CNRS 6279), LM2S, Université de technologie de Troyes, France (Kallas, M.; Honeine, P.) \|\| Laboratoire Fizeau (UMR CNRS 6525), Observatoire de la Côte d'Azur, Université de Nice Sophia-Antipolis, France (Richard, C.) \|\| Université Libanaise, Lebanon (Amoud, H.; Francis, C.)
Abstract	The inherent physical characteristics of many real-life phenomena, including biological and physiological aspects, require adapted nonlinear tools. Moreover, the additive nature in some situations involve solutions expressed as positive combinations of data. In this paper, we propose a nonlinear feature extraction method, with a non-negativity constraint. To this end, the kernel principal component analysis is considered to define the most relevant features in the reproducing kernel Hilbert space. These features are the nonlinear principal components with high-order correlations between input variables. A pre-image technique is required to get back to the input space. With a non-negative constraint, we show that one can solve the pre-image problem efficiently, using a simple iterative scheme. Furthermore, the constrained solution contributes to the stability of the algorithm. Experimental results on event-related potentials (ERP) illustrate the efficiency of the proposed method.
Starting Page	3642
Ending Page	3645
File Size	353915
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Kernel Principal component analysis Electroencephalography Optimization Brain models additive weight algorithm Kernel-PCA pre-image problem non-negativity constraint
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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