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For blindness of the parameter settings in kernel principal component analysis (KPCA), kernel function parameter optimized by particle swarm optimization (PSO) algorithm is proposed, and KPCA is applied to feature extraction in fault diagnosis. The mathematical model of kernel function parameter optimized is constructed firstly, then the PSO algorithm with adaptive accelerate (CPSO) is used to optimize...
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...
Kernel principal component analysis (kernel PCA or KPCA) has been used widely for non-linear feature extraction, dimensionally reduction, and classification problems. However, KPCA is known to have high computational complexity, that is the eigenvalue decomposition of which size equals to the number of samples n. Moreover, in order to calculate projection of vector onto the subspace obtained by KPCA,...
How to choose the best or near kernel function to reduce test error rate is the key of KPCA applied to extract nonlinear feature. In this article, on the basis of research of CA, PSO, we propose a programmer flow of CBPSO used for training kernel function and build CBPSO-KPCA. This approach can effectively optimize kernel function. Simulation results show that produces highly competitive results at...
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