提出一种基于主向量分析(PCA)重建的人脸识别方法。
This paper proposes a face recognition algorithm based on the principle components analysis (PCA) reconstruction.
本文提出了一种有限样本集上基于次特征值误差补偿和优势主向量上非对称分布的马氏距离改进算法。
A modification on Mahalanobis distance on samples of limited size by compensation for errors of non-dominant eigenvalues and asymmetrical distribution on dominant principle components is proposed.
在1999年《新绝地武士团》系列的第一本小说《主向量》里，丘巴卡是为了对抗外星种族遇战疯人而伤亡的众多人员之一。
In 1999's Vector Prime, the first novel of the New Jedi Order series, Chewbacca was one of many casualties in the war against the alien Yuuzhan Vong.
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。
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.
根据3个主成分各自对应的特征向量特点，分别解释为人为因子、温度因子和降水因子并进行回归分析。
According to the corresponding eigenvectors, the 3 principal components were explained as the anthropogenic factor, the temperature factor, and the precipitation factor.
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 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.
它是自然幂迭代方法的一个延伸，不仅跟踪主子空间，而且得到了主特征向量。
It is an extension of the natural power method because it is a solution to obtain the principal eigenvectors as well as to track the principal subspace.
然后构造了在预给极点情况下求主对角线和副对角线上向量值有理插值的矩阵算法。
In addition this paper constructs a matrix algorithm for computing bivariate diagonal vector valued rational interpolants with preassigned poles.
实验结果表明，结合核主成份分析的特征提取，支持向量机方法是一种很有前景的多目标图像分割技术。
This paper investigates the segmentation of multi-target image based on SVM approach combining feature extraction of kernel PCA.
其根据最大输出性能准则，实时自动调整加权向量以实现主波束和方向图零点的优化，通过优化期望用户的主波束、对干扰信号实现零陷提高系统的容量与性能。
It adjusts the weight vectors in order to optimize the main beam and zero sinking of directional pattern real-time and automatically according to maximize exporting criterion.
通过对图像在每个像素的邻域的基础上进行主分量分析，产生每个图像像素的特征向量，再用PCNN对得到的特征图像进行点火分割。
It presents principal component analysis on image based on neighborhood unions of per-pixel to obtain the eigenvector of per-pixel, then USES PCNN to set on fire to segment image with feature image.
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。
By integrating the characteristics of KPCA and SVM, a face recognition method based on these two algorithms is presented.
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。
The dissertation mainly aims at applying support vector machine (SVM) and kernel principal component analysis (KPCA) to intrusion detection.
根据各时段各主成分的特征向量计算出主成分的指标值。并分析得出各个时间段的耕地变化驱动因子。
Indicators of each principal component were calculated according to the eigenvalues of each of them, and driving forces of cultivated land in each period can acquired.
结果表明，葡萄园节肢动物群落、植食类亚群落、捕食类亚群落特征向量矩阵中第1主分量综合指标贡献率依次为66·70%、73·39%和54·17%。
The results showed that in the first principal component, the contribution of arthropod community, phytophagous sub-community and predacious sub-community was 66.70%, 73.39% and 54.17%, respectively.
当支持向量机和主成分分析结合后，试验的损伤识别效果有明显的提高。
The structural damage position and deg ree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.
根据第一主成分和第二主成分的特征向量计算各时间段的第一主成分和第二主成分的指标值。
Then utilizing principal component analysis, the author analyzed the data of each period and concluded that the first and second principal component's accumulative proportion is more than 85 percent.
根据第一主成分和第二主成分的特征向量计算各时间段的第一主成分和第二主成分的指标值。
Then utilizing principal component analysis, the author analyzed the data of each period and concluded that the first and second principal component's accumulative proportion is more than 85 percent.
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