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站长！这是使用LDA和PCA模式识别方法对人脸特征进行提取计算的Matlab程序框架，比较好！-head! This is the LDA and the use of pattern recognition methods on PCA Facial Feature Extraction for the Matlab program, good!
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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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子空间学习的代码，主要包括人脸识别中常用的特征提取算法如pca lda 以及目前常见的流行学习的相关代码-Subspace learning the code, mainly including commonly used in face recognition feature extraction algorithms such as pca lda and the current prevalence of common learning-related code
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该程序包实现了模式识别中的两个特征提取算法，主成分分析PCA和线性判别分析LDA。采用C++语言编写，开发环境VS。 程序包还提供了两个测试样本文件。-The package to achieve the recognition of the two feature extraction algorithm, principal component analysis PCA and linear discriminant analysis LDA. Using C++ language, dev
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matlab图像处理工具相，使用了主成分分析，ANN，SVM等方法。-This toolBox used in the image processing(feature extraction and classification)
PCA,LDA,ICA,DCT,RBF,RBE,GRNN,KNN,minimum distance,SVM, and others
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2DLDA PK LDA for feature extraction
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In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is in
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用matlab编写的lde算法，实现的数据分析，抽取分类信息和压缩特征空间维数-Lde prepared using matlab algorithm to achieve the data analysis, feature extraction classified information and compressed space dimension
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对随机选择的iris数据，用LDA进行特征提取，然后用K近邻分类器分类的完整程序-Feature extraction using LDA,and perform classification via KNN
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用pca 和 lda 实现数据的降维,加快机器的特征提取的速度。-Pca and lda of data with dimension reduction, feature extraction to speed up the speed of the machine.
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用于lda特征脸提取，可以调整参数，选取特征向量-Lda feature extraction for face, you can adjust the parameters, select the feature vector
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Kernel Discriminant Analysis a kernel extension of Linear Discriminant Analysis technique which is a well-known feature extraction technique.-Kernel Discriminant Analysis is a kernel extension of Linear Discriminant Analysis technique which is a well
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svd with LDA face recognition feature extraction
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Fisherface方法的实现是在PCA数据重构的基础上完成的，首先利用PCA将高维数据投影到低维特征脸子空间，然后再在这个低维特征脸子空间上用LDA特征提取方法得到Fisherface。程序中使用参数寻优的方法来寻找最佳投影维数，以达到比较理想的识别效果。-The Fisherface method implemented in the PCA data reconstruction based on the completion of the first use of PCA project
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机器学习中几种典型的降维方法， PCA, LDA等-typical feature extraction methods(PCA, LDA) in machine learning
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PCA特征提取。LDA降维的人脸识别代码。可用作为初学者使用。-PCA feature extraction. Face recognition code LDA dimensionality reduction. Available for beginners to use.
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NWFE:Nonparametric Weighted Feature Extraction for Classification，线性分类方法，类似于改进了了的LDA，使用中，结果有时候比SVM等好-classification, NWFE:Nonparametric Weighted Feature Extraction for Classification
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this paper has used gabor filter for feature extraction and for feature reduction PCA+LDA has been used. for classification minimum distance classifier is used. PCA+LDA shows better performance than PCA or LDA alone
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this for pqrst detection .......................and its classification using lda feature extraction and nntool-this is for pqrst detection .......................and its classification using lda feature extraction and nntool
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csp特征提取，lda进行分类，四分类脑电信号处理，亲测可用(CSP feature extraction, LDA classification, four classification of EEG signal processing, pro test available)
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