The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.
基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。
A method based on multiway kernel principal component analysis (MKPCA) was proposed to capture the nonlinear characteristics of normal batch processes.
为此提出了一种多向核主元分析(MKPCA)算法用于间歇过程的建模与在线监测。
In the training phase, kernel principal component analysis is used to capture nonlinear handwriting variations.
在训练阶段，核-主元分析用来捕捉非线性的手写变化。
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