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These are the codes in \"A note on two-dimensional linear discrimant analysis\", Pattern Recognition Letter
In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assu
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基于线性判别的人脸识别,本设计对主成分分析和线性判别两种子空间方法进行了深入研究,包括LDA及其在人脸识别应用的若干问题-Face recognition based on linear discriminant, the design of the principal component analysis and linear discriminant subspace methods are two in-depth study, including the LDA and its Appl
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Fisher判别的基本思想是将k$个总体的所有$维空间的样本点投影到一维空间上,使投影后组与组之间尽可能的分开,然后利用方差分析的方法推出判别函数。为了简单起见,通常利用线性的判别函数。-The basic idea of Fisher discriminant is k $ total $ dimensional space sample point projection to the one-dimensional space, so that the projection between
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人脸识别的完整代码。能进行主成份分析,混合主成份分析,线性判别分析,弹性图像匹配算法等。-Complete code for face recognition. Principal component analysis, the hybrid principal component analysis, linear discriminant analysis, elastic image matching algorithm.
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线性判别分析LDA人脸识别代码,识别率计算-Linear discriminant analysis LDA face recognition code recognition rate
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里面包含了步态识别的整一套流程的代码实现,包括从视频流里面提取图片帧,背景建模,提取运动目标(运动检测),形态学处理(膨胀与腐蚀,连通性检测),归一化大小,步态能量图的构建,主成分分析(PCA)降维,线性判别分析(LDA)分类等等功能的代码实现。均通过测试。-Contains the gait recognition of the whole process of a set of code, including the extraction and picture frames from th
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LRC是线性表示分类,主要是对图像分类的线性判别分析。-LRC is a linear representation classification, mainly for image classification linear discriminant analysis.
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Fisher线性判别是线性分类算法中最基本的一种算法,其基本思想是将d维空间中的样本投影到一条最易于分类的投影线上,再进行分类-Linear discriminant analysis is one of the most basic algorithm for linear classification algorithm, the basic idea is to divide the sample projection D dimensional space into one of the
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了适应跟踪过程中目标光照条件的变化,并对目标特征进行在线更新,提出一种将局部二元模式(LBP)
特征与图像灰度信息相融合,同时结合增量线性判别分析对目标进行跟踪的算法.跟踪开始前,为了获得比较准确的目标描述,使用混合高斯模型和期望最大化算法对目标进行分割;跟踪过程中,通过蒙特卡罗方法对目标区域和背景区域进行采样,并更新特征空间参数.得到目标和背景的最优分类面;最后使用粒子滤波器结合最优分类面对目标状态进行预测.通过光照变化的仿真视频和自然场景视频的跟踪实验,验证了文中算法的有效性.-Trac
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线性判别式分析(Linear Discriminant Analysis, LDA),也叫做Fisher 线性判别(Fisher Linear Discriminant ,FLD),是模式识别的经典算法,新手学习的良好素材-Linear discriminant analysis (Linear Discriminant Analysis, LDA), also known as Fisher linear discriminant (Fisher Linear Discriminant, FL
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Fisher进行数据线性判别分析,里边包含有原数据,所有程序均运行成功。-Fisher linear discriminant analysis data code. It contains raw data, all programs run successfully.
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用于降维的matlab工具包,包括PCA,LDA,LLE,等-Matlab Toolbox for Dimensionality Reduction
Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Classical multidimensional scaling (MDS)
Sammon mapping
Linear Discriminant Analysis (LDA
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基于LDA线性辨别分析的人脸识别算法,采用KNN分类器,可直接运行,自带数据库,识别率有88 。-LDA face recognition algorithm based on linear discriminant analysis, using KNN classifier, can be directly run, comes with a , the recognition rate of 88 .
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线性判别分析LDA降维算法,类内距离最小,类间距离最大,训练、测试、分类完整的处理流程-Linear discriminant analysis LDA dimensionality reduction algorithm, the smallest within-class distance, the maximum distance between the classes, training, testing, processing complete classification
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matlab平台实现人脸识别,通过PCA降维后再通过线性判别分析LDA实现人脸匹配。内附ORL人脸数据库,运行main函数即可输出结果-Matlab platform to achieve face recognition, PCA dimensionality reduction and then through linear discriminant analysis LDA face matching. Contains ORL face , run the main function t
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ldatrace performs Linear Discriminant Analysis with optional-Ldatrace performs Linear Discriminant Analysis with optional
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ldatrace performs Linear Discriminant Analysis with optional-Ldatrace performs Linear Discriminant Analysis with optional
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ldatrace performs Linear Discriminant Analysis with optional-Ldatrace performs Linear Discriminant Analysis with optional
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ldatrace performs Linear Discriminant Analysis with optional(Ldatrace performs Linear Discriminant Analysis with optional)
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