A new face recognition method based on combination of KPCA features is proposed in this paper.
提出了一种基于核主成分特征组合的人脸识别方法。
Based on ORL face database, recognizes correlation coefficients of principal component extracted by KPCA.
基于OR L人脸库，识别核主成分分析提取出的主成分的相关性系数。
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。
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。
By integrating the characteristics of KPCA and SVM, a face recognition method based on these two algorithms is presented.
结合核主元分析与支持向量机的特点，提出了一种基于核主元分析与支持向量机的人脸识别方法。
After upper facial action unit location and segmentation, we present the facial action unit feature extraction algorithm based on KPCA.
在定位分割出上半人脸运动单元子区域图像之后，提出了采用KPCA算法提取它们的特征。
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。
KPCA extracted principal component with nonlinear method and described the relationship among three or more pixels of the identified images.
KPCA采用非线性方法提取主成分，描述待识别图像中多个像素之间的相关性。
The experiment results concludes that the SVM classification method based on KPCA have the better classification effect than the other three.
实验结果表明，基于KPCA特征提取法的支持向量机分类器的分类错误率在这四种分类算法中最低。
In the paper, GBGM-GA is seen the optimization technique combining KPCA and GA, and is suitable to the optimization selection of kernel function parameter.
本文采用高斯变异遗传算法作优化技术，实现了KPCA和GA的集成，适合核函数参数的优化选择。
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。
When applied to process monitoring, the FSKPCA-based method is more efficient in computation and needs less computer memory than standard KPCA-based methods.
与标准KPCA方法相比，FSKPCA方法具有更高的计算效率且只需较小的计算机存储空间。
The experimental comparisons show that this algorithm outperforms traditional KPCA and K-Nearest Neighbor classifier on both feature extraction and classification.
通过实验比对可知该算法效果在特征提取和分类方面均优于传统核主成分分析法以及最近邻分类器。
After clustering analysis by the improved FCM, the obtained cluster centers as input samples is used and then the principal component images can be obtained based on KPCA.
首先利用改进的FCM进行聚类分析，然后将获得的聚类中心作为输入样本，进行KPCA，从而得到主成分图像。
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。
Firstly KPCA is used to extract the features of human face image, and then SVM combined with the nearest distance rule is used for classification, which depends on the kernel principal components.
该方法首先利用核主元分析对人脸图像进行特征提取，然后依据支持向量机与最近邻准则对所提取的核主元特征进行分类识别。
Firstly, it constructs a fitness function which Fisher discriminate function is optimized object, then WCPSO is used to optimize it by its many random particles to improve the performance of KPCA.
建立以Fisher判别函数为优化目标的适应度，利用粒子群算法中多个随机粒子实现核函数参数的优化，改善了核主元分析方法的性能。
Completed work is summarized as following: The paper gives a integrated research based on PCA from fault detection, fault diagnosis, reconstruction fault to a new fault detection method based on KPCA.
主要内容如下：对基于主元分析的方法进行了综合的研究：从故障检测、故障诊断、故障重构以及基于核主元分析的故障检测方法。
Completed work is summarized as following: The paper gives a integrated research based on PCA from fault detection, fault diagnosis, reconstruction fault to a new fault detection method based on KPCA.
主要内容如下：对基于主元分析的方法进行了综合的研究：从故障检测、故障诊断、故障重构以及基于核主元分析的故障检测方法。
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