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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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人脸检测源代码. The souce demonstrates face detection SSE optimized C++ library for color and gray scale data with skin detection, motion estimation for faster processing, small sized SVM and NN rough face prefiltering, PCA/LDA/ICA/any dimensionality reduct
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PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果-PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results
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子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the
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主成分分析，对多特征数据进行主成分分析，降低样本的维度，实现分类前的预处理。-Principal component analysis, principal component analysis was carried out on the characteristic data, reduce the dimension of sample pretreatment before implement classification.
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K—mean分类和主成分分析法的应用实例，很明晰的讲解实例，非常适合算法应用的学习-Application examples Kmean classification and principal component analysis, it is clear to explain instance, very suitable learning algorithm
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模式分类工具箱，有PCA、SVM、ID3源代码，用于数据分析、模式识别和机器视觉。-Pattern classification toolbox, there PCA, SVM, ID3 source code for data analysis, pattern recognition and machine vision.
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本文档包含了对视频分类的方法论文，先提取视频中音频信息和图像信息，然后进行拼接并使用PCA进行降维处理，最后使用高斯联合模型进行学习和分类-This document contains papers on the video classification method, first extract the video audio information and image information, and then stitching using PCA dimension reduction,
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图像处理与识别实现分类算法功能的函数PCA（主成分分析）方法-PCA (principal component analysis) function of image processing and recognition to achieve classification algorithm
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matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值--attern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
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pca源程序代码，主成分分析，用于高维数据姜维，进一步用于数据分类模式识别-pca source code, principal component analysis, Jiang Wei for high-dimensional data, further data for pattern recognition classification
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用于主成分图像svm分类，简单，有很好的程序，适合初学者(SVM for principal component image classification, simple, there are very good procedures for beginners)
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This toolbox is meant to facilitate the manipulation of images and video in Matlab. Its purpose is to complement, not replace, Matlab's Image Processing Toolbox, and in fact it requires that the Matlab Image Toolbox be installed. Emphasis has been pl
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A two-stage mechanism of ECG classification using Gaussian
mixture model(An automatic classifier for electrocardiogram (ECG) based cardiac abnormality detection using Gaussian mixture model (GMM) is presented here. In first stage, preprocessing tha
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The pca method was used for the classification of BP neural network
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人脸识别，降维 加分类，主成分分析降维，支持向量机分类(Face recognition, principal component analysis reduced Vega classification, dimension reduction, support vector machine classification)
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PCA is used for data classification
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主成分分析，提取图像中的SIFT特征点，用于图像识别和分类(Principal component analysis (PCA), extracting SIFT feature points in images for image recognition and classification)
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用于脑电信号分析的matlab算法，对数据进行PCA处理及SVM分类。(The matlab algorithm for EEG signal analysis performs PCA processing and SVM classification on data.)
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对碰撞信号特征进行降维和聚类分析，提高分类精度(Reducing dimension and clustering analysis of collision signal features to improve classification accuracy)
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