The optimal width parameter a in formula (10) of Gauss radial basis
kernel principal component analysis (GRB-KPCA) was determined by cross validation.
Peng, "Fault identification method of
kernel principal component analysis based on contribution plots and its application," Systems Engineering and Electronics, vol.
Qi, "The fault feature extraction and classification of gear using principal component analysis and
kernel principal component analysis based on the wavelet packet transform," Measurement, vol.
In this paper, an improved
kernel principal component analysis method based on spare representation is proposed for more performance in face recognition.
The historical datum of input variables in the soft-sensor model is carried out by
kernel principal component analysis, whose results are described in the Table 3.
Kernel principal component analysis (KPCA), first proposed by Scholkopf et al.
The representative methods are
kernel principal component analysis (KPCA) [4] and kernel Fisher discriminant analysis (KFDA) [5], which are the kernel extensions of PCA and LDA, respectively.
Kernel trick has demonstrated huge success in modelling real-world data with highly complex nonlinear structures, such as support vector machine (SVM) [35], kernel linear discriminant analysis (KLDA) [36], and
kernel principal component analysis (KPCA) [37],
The non-linear methods include the kernel version of above-mentioned methods i.e., Kernel Discriminating Analysis (KDA) [5],
Kernel Principal Component Analysis (KPCA) [6], Laplacian faces [7] and many others.
The approach has been studied and extended some kernel based algorithms such as
kernel principal component analysis (KPCA) [29] and kernel fisher discriminant analysis (KFD)[30, 31].