Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.
其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。
A new image feature extraction method based on changing image matrices and image principal component analysis (CIMPCA) is proposed in this paper.
在图像主分量分析的基础上，提出了一种基于图像矩阵变换的主分量分析方法。
In the process of test, principal component analysis is used as data preprocessing to extract the feature index from vibration signal statistic features as the input of SVDD classifier.
在测试的过程中，主成分分析作为数据预处理，提取特征指数从振动信号的统计特征作为输入SVDD分类。
Considering the feature of left matrix fraction description, this article presents a construction method of the stabilization diagram by principal component analysis (PCA).
针对左矩阵分式模型的特点，给出了一种通过主分量分析(PCA)建立稳定图的方法。
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.
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。
This second edition has been completely revised to feature new chapters on principal component analysis, self-modeling curve resolution, and multi-way analysis methods.
这次再版已经被完全修正成特征关于主成分分析，自我模型化曲线决定和多模式的分析方法的新章。
On the above basis, we used principal component analysis of the "five factors" for feature extraction and reduced the input dimension of BP network importation.
在此基础上，采用主成分分析法对“五因素”进行特征提取，降低BP网络的输入维度。
Based on principal component analysis (pca) Face Feature Extraction MATLAB implementation.
基于主元分析(pca)的人脸特征提取MATLAB实现。
The proposed method is composed of the following three parts:(1) The feature space is reduced by the PCA(the principal component analysis) on the normalized input spectra;
该方法包括以下几个步骤：（1）先将训练样本归一化，再利用PCA变换进行降维，获得样本特征向量；
The texture images are processed by principal component analysis(PCA), and the feature vectors are selected.
根据深度图像测定的目标角度，对三维目标灰度图像在其所属特征空间进行分解与重构。
The texture images are processed by principal component analysis(PCA), and the feature vectors are selected.
根据深度图像测定的目标角度，对三维目标灰度图像在其所属特征空间进行分解与重构。
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