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本程序采用线性判别分析方法实现的字符识别,达到较高的识别率。并给出了很多实验得到的识别结果-the adoption of linear discriminant analysis method of character recognition, reach a higher recognition rate. Given the many experimental results Recognition
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利用fisher线性判别分析进行数据降维-using linear discriminant analysis of data dimensionality reduction
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这里面包含两个文件,可以实现主成分分析(PCA)算法和线性判别分析(LDA)算法。不过还需要CMatrix类的定义。-which contained two documents, can be achieved principal component analysis (PCA) algorithm and linear discriminant analysis (LDA) algorithm. But also need CMatrix class definition.
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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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正交线性判别分析(Orthogonal Linear Discriminant Analysis),可以用于数据降维上面。-orthogonal linear discriminant analysis (Orthogonal Linear Discriminan t Analysis), can be used for cutting down the data above.
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基于奇异值分解的核线性判别分析(Kernel Discriminant Analysis via QR Decomposition)。
-based on the singular value decomposition of the nuclear linear discriminant analysis (Kernel Discriminant An alysis via QR Decomposition).
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LDA/GSVD算法matlab代码,可用来实现基于广义奇异值分解的线性鉴别分析方法-LDA / GSVD algorithm Matlab code, can be used to achieve based on the generalized singular value decomposition of linear discriminant analysis method
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ldatrace performs Linear Discriminant Analysis with optional constraints on the transform matrix
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PCA---主成分分析 LDA---线性区别分析此类实现结合两者的有缺点实现图像模式识别,其中需要有矩阵类-PCA principal component analysis --- --- LDA linear discriminant analysis combining the two to achieve such a flawed it Image is pattern recognition, which requires matrices
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Linear Discriminant Analysis算法,此压缩包中已经带有大量train和test的相关图片,用于做人脸识别。LDA算法也可以用于其他领域如语音信号处理,此代码仅供研究,请勿用于商业!
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Matlab source codes for the regularized linear discriminant analysis (R-LDA),Author: Lu Juwei,Bell Canada Multimedia Lab, Dept. of ECE, U. of Toronto,Released in 01 November 2004
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Linear Discriminant Analysis in matlab
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线性决策分析,一个最经典的线性降维方法,也是最经典的最简单的监督学习算法,A CLASSICAL LINEAR DISCRIMINANT ANALYSIS
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线形判别分析算法和knn最近邻算法的实现,Linear discriminant analysis algorithm and KNN nearest neighbor algorithm
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使用matlab实现的LDA(线性判别分析)分类器,以及PCA的实现-Implemented using matlab LDA (linear discriminant analysis) classifier, and the implementation of PCA
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Face Recognition, Face Detection, Lausanne Protocol, 3D Face Reconstruction,
Principal Component Analysis, Fisher Linear Discriminant Analysis,
Locality Preserving Projections, Kernel Fisher Discriminant Analysis,Face Recognition, Face Detection, L
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该程序包实现了几个常用的模式识别分类器算法,包括K近邻分类器KNN、线性判别方程LDF分类器、二次判别方程QDF分类器、RDA规则判别分析分类器、MQDF改进二次判别方程分类器、SVM支持向量机分类器。 主程序中还有接口调用举例,压缩包中还有两个测试数据集文件。-The package to achieve a number of commonly used pattern recognition classifier algorithms, including K neighbor class
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线性判别分析(LDA)用于特征选择,可以对数据集或者图像提取有用特征,用于分类或者聚类等机器学习应用中-Linear Discriminant Analysis (LDA) for feature selection, application in dataset or image feature extraction, for classification or clustering applications in machine learning
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改进型线性判别分析以及新提出的关于两个变量之间距离的比较方法
-Improved linear discriminant analysis and a new method for the comparison of the distance between two variables
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ldatrace performs Linear Discriminant Analysis with optional-Ldatrace performs Linear Discriminant Analysis with optional
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