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。
On the basis of analysis of several methods for modeling, a soft sensor based on kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed.
在具体分析了多种建模方法的基础上，提出了核主元分析结合最小二乘支持向量机软测量建模方法。
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。
One new method for fault diagnosis of steam turbine based on kernel principal component analysis (KPCA) and multistage neural network ensemble was proposed.
提出一种基于核主元分析(KPCA)和多级神经网络集成的汽轮机故障诊断方法。
The nonlinear components of gait features are extracted based on kernel principal component analysis (KPCA).
在训练阶段，核-主元分析用来捕捉非线性的手写变化。
The nonlinear components of gait features are extracted based on kernel principal component analysis (KPCA).
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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