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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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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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这是一个MATLAB工具箱包括32个降维程序,主要包括 pca,lda,MDS等十几个程序包,对于图像处理非常具有参考价值- ,This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techn
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特征提取是模式识别研究领域的一个热点.本文提出了一种基于独立成分分析和线性鉴别分析的特征提取方法.谊方法中引入了零空间的概念,指出了前人算法中的不足之,并且给出了一个完整的独立成分分析和线性鉴别分析的组合算法.在ORL和Yale人脸数据库上的实验表明了该方法的有效性.-Feature extraction is a hot field of pattern recognition research. In this paper, which is based on independent com
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Integrating Independent Components and Linear Discriminant Analysis
for Gender Classification
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偏最小二乘线性判别分析, 以及变量选择方法-partial least squares-linear discriminant analysis
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二维线性鉴别分析(2DPLA)算法的C++实现,可以用于实际工作和学习中-Two-dimensional linear discriminant analysis (2DPLA) C++, the algorithm can be used in practical work and learning
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线性鉴别分析,是信号处理中一种用来筛选所需信号的常用方法。其基本思想是选择使得Fisher准则函数达到极值的向量作为最佳投影方向,从而使得样本在该方向上投影后,达到最大的类间离散度和最小的类内离散度。在Fisher思想的基础上,Wilks和Duda分别提出了鉴别矢量集的概念,即寻找一组鉴别矢量构成子空间,以原始样本在该子空间内的投影矢量作为鉴别特征用于识别。-Linear Discriminant Analysis:Fisher R A. The use of multiple measurem
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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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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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Fisher 线性判别分析算法matlab实现及感知器算法实现-Fisher linear discrimination analysis,perceptron
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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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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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