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。
An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA).
提出了基于核函数主元分析的齿轮故障诊断方法。
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。
The dissertation mainly aims at applying support vector machine (SVM) and kernel principal component analysis (KPCA) to intrusion detection.
本文的主要工作是将支持向量机(SVM)及核主成分分析(KPCA)应用到入侵检测技术中。
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。
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。
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。
In this paper, kernel independent component analysis (KICA) 's principle and algorithm are introduced, and then the KICA comparison with some other ICA and principal component analysis (PCA) is given.
论文介绍了基于核空间的ICA的原理和基本算法，然后介绍了该算法与典型ICA和主成分分析(PCA)在盲源信号分离中的比较。
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。
About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.
多元统计过程介绍了三种主要的方法：主元分析法、偏最小二乘法和核函数概率密度估计法。
About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.
多元统计过程介绍了三种主要的方法：主元分析法、偏最小二乘法和核函数概率密度估计法。
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