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Communication Dans Un Congrès Année : 2010

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

Résumé

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.
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Dates et versions

hal-01966047 , version 1 (27-12-2018)

Identifiants

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Maya Kallas, Paul Honeine, Cédric Richard, Hassan Amoud, Clovis Francis. Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image. Proc. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, Buenos Aires, Argentina. pp.3642-3645, ⟨10.1109/IEMBS.2010.5627421⟩. ⟨hal-01966047⟩
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