Description: （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；
（2）将所有子图象按列相接变成一个81维的行向量；
（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；
（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。
（5）求出所有子块的特征向量。
-(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector. Platform: |
Size: 64512 |
Author:ly |
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Description: matlab编写的Modular PCA的源代码，以Yale人脸库为例。Moduplar PCA首先对原始图像分块，然后对分块后的所有子图像进行PCA分析提取投影特征，对待识别图像也是先进行分块，然后分别计算子图像在投影特征下的投影系数，最后根据最近邻分类器进行分类。-Modular PCA prepared matlab source code to Yale face database as an example. Moduplar PCA first block of the original image, and then on the block after all the sub-image analysis to extract PCA projection characteristics, treatment identification image is first block, and then calculated the characteristics of sub-image in the projector under the projection coefficient, and finally the light of recent Neighbor Classifier classification. Platform: |
Size: 2048 |
Author:章格 |
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Description: 利用Sub-pattern PCA在Yale人脸库上进行人脸识别的matlab源代码，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-pattern PCA use in the Yale face database for face recognition on the matlab source code, 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 the use of 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: 2048 |
Author:章格 |
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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: This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation-This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation Platform: |
Size: 226304 |
Author:tra ba huy |
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Description: Amir Hossein Omidvarnia用matlab编写的基于PCA的人脸识别系统和基于FLD的人脸识别系统,其中
的图像示例为Essex face database中 face94 的部分图像,文献可参考"Eigenfaces vs. Fisherfaces:
Recognition using class specific linear projection."已经测试过程序可正常运行没有问题。-Amir Hossein Omidvarnia prepared using matlab Face Recognition System Based on PCA and FLD-based face recognition systems, which sample the image of Essex face database for ' face94' part of images, documents may refer to " Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. " procedures have been tested there is no problem to normal operation. Platform: |
Size: 377856 |
Author:刘子木 |
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Description: The important method is [COEFF,SCORE] = princomp(x) which takes in your data “x” and stores its projection into PCA space in “SCORE” which I then output to csv. I still need to find out how to project back into normal space but I think it should be just as straightforward as this was. For more info on “princomp” type “help princomp” into matlab and have a look at the help files.-The important method is [COEFF,SCORE] = princomp(x) which takes in your data “x” and stores its projection into PCA space in “SCORE” which I then output to csv. I still need to find out how to project back into normal space but I think it should be just as straightforward as this was. For more info on “princomp” type “help princomp” into matlab and have a look at the help files. Platform: |
Size: 2048 |
Author:barakkath |
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Description: 用Matlab来实现PCA，并分别求出图像在第一、二、三主分量上的投影。-Using Matlab to implement PCA, and images were obtained in the first, second and third principal component on the projection. Platform: |
Size: 5351424 |
Author:liwei |
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Description: 用LDA及pca算法分析特征，选择最好的特征。-This program uses LDA and PCA to analyze features from weka arff file. The projection on PCA and LDA space visualizes the goodness of the features. If the features are good enough to be classified well they should have some kind of separation when projected on a 1 dimensional LDA or a 3 dimensional PCA space.
This MATLAB script assumes that the arff file has 2 classes named "Positive" and "Negative". However, it can be extended into any amount of class labels.
Platform: |
Size: 369664 |
Author:易和 |
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Description: 多元数据分析的主分量分析投影，进行逐步线性回归，对于初学matlab的同学会有帮助，是学习PCA特征提取的很好的学习资料，三相光伏逆变并网的仿真，matlab开发工具箱中的支持向量机。-Principal component analysis of multivariate data analysis projection, Stepwise linear regression, Matlab for beginner students will help, Is a good learning materials to learn PCA feature extraction, Three-phase photovoltaic inverter and network simulation, matlab development toolbox support vector machine. Platform: |
Size: 10240 |
Author:iccvmn |
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Description: 本程序的性能已经超过其他算法，使用大量的有限元法求解偏微分方程，结合PCA的尺度不变特征变换(SIFT)算法，多元数据分析的主分量分析投影，基于matlab GUI界面设计。-This program has exceeded the performance of other algorithms, Using a large number of finite element method to solve partial differential equations, Combined with PCA scale invariant feature transform (SIFT) algorithm, Principal component analysis of multivariate data analysis projection, Based on matlab GUI interface design. Platform: |
Size: 4096 |
Author:eafzdzqe |
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Description: 基于欧几里得距离的聚类分析，多元数据分析的主分量分析投影，是学习PCA特征提取的很好的学习资料，Matlab实现界面友好，本程序的性能已经超过其他算法，有详细的注释，匹配追踪和正交匹配追踪。- Clustering analysis based on Euclidean distance, Principal component analysis of multivariate data analysis projection, Is a good learning materials to learn PCA feature extraction, Matlab to achieve user-friendly, This program has exceeded the performance of other algorithms, There are detailed notes, Matching Pursuit and orthogonal matching pursuit. Platform: |
Size: 6144 |
Author:wgmxqud |
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Description: 借鉴了主成分分析算法（PCA），结合PCA的尺度不变特征变换(SIFT)算法，线性调频脉冲压缩的Matlab程序,多元数据分析的主分量分析投影，模拟数据分析处理的过程，处理信号的时频分析，预报误差法参数辨识-松弛的思想，FIR 底通和带通滤波器和IIR 底通和带通滤波器。- It draws on principal component analysis algorithm (PCA), Combined with PCA scale invariant feature transform (SIFT) algorithm, LFM pulse compression of the Matlab program, Principal component analysis of multivariate data analysis projection, Analog data analysis processing, When processing a signal frequency analysis, Prediction Error Method for Parameter Identification- the idea of relaxation, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter. Platform: |
Size: 9216 |
Author:strupn |
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Description: 结合PCA的尺度不变特征变换(SIFT)算法，采用加权网络中节点强度和权重都是幂率分布的模型，一些自适应信号处理的算法，matlab编写的元胞自动机，多元数据分析的主分量分析投影，isodata 迭代自组织的数据分析，课程设计时编写的matlab程序代码。- Combined with PCA scale invariant feature transform (SIFT) algorithm, Using weighted model nodes in the network strength and weight are power law distribution, Some adaptive signal processing algorithms, matlab prepared cellular automata, Principal component analysis of multivariate data analysis projection, Isodata iterative self-organizing data analysis, Course designed to prepare the matlab program code. Platform: |
Size: 5120 |
Author:wqgmkrk |
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