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.
Mots clés
machine learning
Hilbert spaces
iterative methods
medical signal processing
nonlinear feature extraction
kernel principal component analysis
kernel Hilbert space
pre-image technique
iterative scheme
event-related potentials
ERP
EEG
Feature extraction
Kernel
Electroencephalography
Optimization
Brain models
Kernel-PCA
pre-image problem
non-negativity
constraint
additive weight algorithm
Brain
Brain Mapping
Diagnosis
Computer-Assisted
Evoked Potentials
Humans
Nonlinear Dynamics
Pattern Recognition
Automated
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity
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