Description: 模式识别PCA(principle component analysis)源码.matlab 格式。PCA为经典而且经常使用的算法。-pattern recognition PCA (principle component analysis) source. Matlab format. PCA to the classic and often use the algorithm. Platform: |
Size: 1495 |
Author:吴东 |
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Description: 模式识别PCA(principle component analysis)源码.matlab 格式。PCA为经典而且经常使用的算法。-pattern recognition PCA (principle component analysis) source. Matlab format. PCA to the classic and often use the algorithm. Platform: |
Size: 1024 |
Author:吴东 |
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Description: PCA(主成分分析法)、LDA(线性判别法)
两种方法是主要的线性降维法，有很好的效果，希望对大家能够有用！-PCA (Principal Component Analysis), LDA (Linear Discriminant method) two methods are the main linear dimensionality reduction method, have very good results, in the hope that everyone can be useful! Platform: |
Size: 3072 |
Author:ruchong |
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Description: 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 establishment of sub-image set, in each sub-image set to use PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
Size: 165888 |
Author:tanghui |
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Description: 独立成分分析( I C A) 是一项把混合信号分解成具有统计独立性成分的新技术 。I C A近年已在生物医
域的信号分离中展示 了很好的应用前景 。 我们比较系统地介绍了 I C A的基本原理 、 主要算法 、 应用和
究的发展方向，旨在进一步推动有关的理论与应用研究工作。-Independent Component Analysis (ICA) is a decomposition of the mixed-signal components into a statistical independence of the new technologies. In recent years, ICA has been in bio-medical domain signal separation show a very good application prospects. We systematically introduced the basic principle of ICA, the main algorithms, applications and the development direction of research aimed at further promoting the application of the theory and research. Platform: |
Size: 268288 |
Author:金振东 |
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Description: 它是SourceForge上的一个开源项目，使用Malib实现实时处理，CSU Face Identification Evaluation System进行人脸识别。算法包括：主成份分析(principle components analysis (PCA)),a.k.a eigenfaces算法,混合主成份分析,线性判别分析(PCA+LDA),图像差分分类器(IIDC),弹性图像匹配算法(EBGM)等等
Malic is realtime face recognition system that based on Malib and CSU Face Identification Evaluation System (csuFaceIdEval). Uses Malib library for realtime image processing and some of csuFaceIdEval for face recognition.-It is a SourceForge open source project, using real-time Malib processing, CSU Face Identification Evaluation System for Face Recognition. Algorithms include: Principal component analysis (principle components analysis (PCA)), aka eigenfaces algorithm, mixed-principal component analysis, linear discriminant analysis (PCA+ LDA), the image difference classifier (IIDC), a flexible image-matching algorithm (EBGM), etc. such as Malic is realtime face recognition system that based on Malib and CSU Face Identification Evaluation System (csuFaceIdEval). Uses Malib library for realtime image processing and some of csuFaceIdEval for face recognition. Platform: |
Size: 1326080 |
Author:乔超 |
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Description: This is a MFC program to test Principle Component Analysis (PCA) for constructing Eigenfaces. Using train images, it calculates Eigen values and Eigen vectors with sorting. Then reconstruct test images from PCA coefficients. Platform: |
Size: 5613568 |
Author:SUNGWOONG KIM |
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Description: 神经计算的实验作业。用principle components analysis计算模式的主分量。提取线性输入的特征。-Neural computing experiment operations. Computing model using principle components analysis of the principal component Platform: |
Size: 1024 |
Author:萧茅律 |
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Description: 利用Matlab编程实现主成分分析，
Cwstd.m——用总和标准化法标准化矩阵
Cwfac.m——计算相关系数矩阵；计算特征值和特征向量；对主成分进行排序；计算各特征值贡献率；挑选主成分（累计贡献率大于85 ），输出主成分个数；计算主成分载荷
Cwscore.m——计算各主成分得分、综合得分并排序
Cwprint.m——读入数据文件；调用以上三个函数并输出结果
-The use of principal component analysis Matlab programming, Cwstd.m standardization by the sum of the standardized method to calculate the correlation coefficient matrix Cwfac.m matrix computing eigenvalues and eigenvectors sort of the main components calculate the eigenvalues of the contribution rate selection Principal component (cumulative contribution rate is greater than 85 ), the number of output main component calculating the principal component load Cwscore.m calculate the principal component scores, total score and sort Cwprint.m reads data files calls for more than three function and outputs the result Platform: |
Size: 34816 |
Author:吴耕泓 |
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