Eurasian winter sea level pressure anomalies during 1948-2007 were investigated by applying a nonlinear principal component analysis (NLPCA) method.
运用非线性主成分分析法对欧亚地区1948—2007年冬季海平面气压距平场进行分析。
Eurasian summer sea level pressure anomalies during 1948 -2007 were investigated by applying a Nonlinear Principal Component Analysis (NLPCA) method.
运用非线性主成分分析法对欧亚地区1948—2007年冬季海平面气压距平场进行分析。
As principal component analysis mainly use the linear correlation of the data, we propose a nonlinear principal component analysis method, by combining the mercer kernel function with it.
主成分分析方法主要利用数据的线性相关性来降维，并不适合非线性相关的情况。
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。
Principal component analysis is a linear method, but the most data are nonlinear.
线性主成分分析是一种线性分析方法，而数据通常是非线性的。
Experimental results show that principal curve component analysis is excellent for solving nonlinear principal component problem, and it has great applications potentials.
仿真实验结果表明，主曲线成分分析能很好地解决非线性主成分问题，应用前景广阔。
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。
On this basis, this paper introduce an analysis of ordinal data on nonlinear principal component method.
在此基础上，本文引入了分析有序数据的非线性主成分方法。
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。
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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