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现有的代数特征的抽取方法绝大多数采用一维的方法,即首先将图像转换为一维向量,再用主分量分析(PCA),Fisher线性鉴别分析(LDA),Fisherfaces式核主分量分析(KPCA)等方法抽取特征,然后用适合的分类器分类。针对一维方法维数过高,计算量大,协方差矩阵常常是奇异矩阵等不足,提出了二维的图像特征抽取方法,计算量小,协方差矩阵一般是可逆的,且识别率较高。-existing algebra feature extraction method using a majority of th
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基于PSO训练SVM的人脸识别
利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径-PSO-based SVM for face recognition training using support vector machine learning ability in the performance of good performance, combined with KPCA
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核主成分分析方法,是主成分分析的一种改进算法,是一种非线性的特征提取方法。
-Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method.
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在模式识别中,经常用到的一种提取特征的方法——主成分分析法-In pattern recognition, a frequently used feature extraction method- principal component analysis
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KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取,降维使用.-KPCA major noise in the image to have the application. You can also feature extraction using dimension reduction.
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kpca原始程序和小波去噪部分,用于数据降维和特征提取比较实用-kpca part of the original program and wavelet denoising for data dimensionality reduction and feature extraction more practical
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KPCA程序,可用于数据降维,特征提取,用起来比较简单-KPCA procedure can be used for data dimensionality reduction, feature extraction, using relatively simple
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kernel PCA feature extraction
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Kernel Principal Conponent Analysis 特征提取-Kernel Principal Conponent Analysis feature extraction
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A new method for performing a nonlinear form of Principal
Component Analysis proposed. By the use of integral operator kernel
functions, one can eciently compute principal components in high{
dimensional feature spaces, related to input space
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一系列降维算法,包括LGE,KGE,KLPP,OLEG,KPCA,将高维空间的数据降低的低维,主要用来进行人脸识别以等方面的特征提取,总共5个算法。-isometric projection to feature extraction
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基于核的主成分分析是一种非线性特征提取方法,它通过一个非线性映射将数据从输入空间映射到特征空间,然后在特征空间中进行通常的主成分分析,其中的内积运算采用一个核函数来代替-Core-based principal component analysis is a nonlinear feature extraction method, which maps data the input space to the feature space through a nonlinear mapping,
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主成分分析的一种改进算法,是一种非线性的特征提取方法。(An improved algorithm of principal component analysis is a nonlinear feature extraction method)
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一种特征提取方法:结合主元分析(PCA)和核主元分析(KPCA)的前馈神经网络(FNN)(A feature extraction method: the feedforward neural network (FNN) combined with principal component analysis (PCA) and kernel principal component analysis (KPCA))
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