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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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PCA和KPCA程序,matlab实现,可用于模式识别时做降维或特征提取处理-PCA and KPCA program, matlab implementation, pattern recognition can be used to do when dealing with dimensionality reduction or feature extraction
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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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实现2维核PCA的图像特征提取及识别功能-2-dimensional KPCA image feature extraction and recognition
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一个包含常用特征提取方法的matlab工具包。内含 PCA, CCA, mnf, pls 、 KPCA, KCCA, kmnf, kpls 等算法的实现源码-a matlab toolkit Containing some common feature extraction methods . Containing PCA, CCA, mnf, pls, KPCA, KCCA, kmnf, kpls algorithm implementation source
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基于核函数的SAR图像目标识别,利用KPCA对SAR图像进行特征提取-SAR image target recognition based on kernel function,Use KPCA SAR image feature extraction
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核主成分分析是一种流行的非线性特征提取方法-
Kernel principal component analysis is a popular nonlinear feature extraction method
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主成分分析(Principle Component Analysis, PCA)是最为常用的特征提取方法,被广泛应用到各领域,如图像处理、综合评价、语音识别、故障诊断等。-Principal component analysis (Principle Component Analysis, PCA) is the most commonly used feature extraction methods are widely applied to various fields, such as
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Kpca,matlab编写代码,程序,特征提取好方法,值得一看。-Kpca, matlab code, and procedures, good feature extraction method, worth a look.
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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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简单特征提取算法:PCA,MNF,PLS,CCA,KPCA,KMNF,KPLS,KCCA等-simple algorithm feature extraction ,for example: PCA,MNF,PLS,CCA,KPCA,KMNF,KPLS,KCCA
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this principle component analysis(PCA) that is used for linear dimensionality reduction and feature extraction as well as the nonlinear form of PCA which is KPCA and how PCA is used for Active shape models -this is principle component analysis(PCA) t
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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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使用kpca方法进行特征提取,用作机器学习的第一步(Use kpca method for feature extraction, as the first step in machine learning)
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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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