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快速的人脸特征提取算法KPCA,比普通的pca特征提取算法在效率上好了不少,Fast facial feature extraction algorithm KPCA, than ordinary PCA feature extraction algorithm in the efficiency of a good many
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一个很好的PCA程序。它可用于数据的降维,消噪及特征提取。,A good PCA procedures. It can be used for data dimensionality reduction, de-noising and feature extraction.
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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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在特征提取阶段,研究了PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA等多
种方法。不同于基于图象向量的PCA特征提取,由于2DPCA, (2D) ZPCA, DiagPCA和
DiagPCA-I-2DPCA的特征提取都直接基于图象矩阵,计算量小,所以特征的提取速度明
显高于PCA方法。-In the feature extraction stage, the study of the PCA, 2DPCA, (2D) 2PCA,
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这是一段简单的基于主元分析法的特征提取的程序-This is a simple method based on principal component analysis of the feature extraction procedure
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别人的东西,有关KPCA特征提取的,看过了,很好很强大-Other people' s things, the KPCA feature extraction, and seen, very good very strong
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基于matlab的二维图像的KPCA特征提取-KPCA feature extraction from image by matlab
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用于人脸识别特征提取的KPCA算法,很好的程序,有问题大家交流-KPCA for face recognition feature extraction algorithm, a very good program, there are problems we share
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模式识别特征提取,及扩展后的和特征提取,处理图像的时候可以考虑-Pattern recognition feature extraction, and expanded and feature extraction, image can be considered
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kPCA程序,输入相应的参数后,可以直接运行,能应用到图像的特征提取与去噪等方面-kPCA program input parameters can be run directly, can be applied to image feature extraction and denoising
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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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子空间的方法,KPCA matlab程序,可以实现对掌纹图像的特征提取和匹配。-Subspace methods, KPCA matlab program, you can achieve the palmprint image feature extraction and matching.
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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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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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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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