Description: KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取，降维使用 -KPCA major noise in the image to have the application. In addition can also be used for feature extraction, dimensionality reduction using Platform: |
Size: 1024 |
Author:videohu |
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Description: 此程序是进行人脸识别时，进行非线性降维后提取特征的一个典型方法，想做非线性降维特征提取的朋友可以研究，本人看后收获颇多-This procedure is carried out face recognition, the non-linear dimensionality reduction after the extraction of the characteristics of a typical method to do non-linear dimensionality reduction Friend feature extraction can be studied, I watch a lot of post-harvest Platform: |
Size: 589824 |
Author:ttt |
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Description: PCA算法，用于人脸识别的特征提取步骤，是目前最通用的传统的人脸识别算法。通过构建特征子空间进行降维-PCA algorithm for face recognition feature extraction step is currently the most common in traditional face recognition algorithms. By constructing feature subset space dimensionality reduction Platform: |
Size: 1024 |
Author:Jessie |
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Description: LDA 的matlab源码实现，Jonathan Huang编写的，是目前唯一可用于且正确的源码，可用于图像特征降维-LDA to achieve the matlab source, Jonathan Huang, prepared, is the only can be used and the correct source, can be used for image feature dimensionality reduction Platform: |
Size: 3072 |
Author:唐颖军 |
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Description: 一个很好的PCA程序。它可用于数据的降维，消噪及特征提取。-A good PCA procedures. It can be used for data dimensionality reduction, de-noising and feature extraction. Platform: |
Size: 2048 |
Author:xiaolinzi |
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Description: 独立分量分析的算法，用于分离出独立分量，用于图像降维，特征提取-Independent component analysis algorithms, used to separate out the independent component for the image dimensionality reduction, feature extraction Platform: |
Size: 173056 |
Author:cecilia |
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Description: （压缩包里一共有5个代码）
pca+lda+粗糙集+模糊神经网络
saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat
1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat
2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat
3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat
4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat
5.用runfnn.m对rstest进行测试得到其识别率
savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.-pca+ lda+ Rough Set+ fuzzy neural network
saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat
Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat
2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat
3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat
4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat
5. Rstest used to test for runfnn.m by its recognition rate
cm.m is savem.m and minimum distance classifier on the training set and test set classificati Platform: |
Size: 2048 |
Author:dong |
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Description: 快速PCA计算方法，有效实现降维等操作，和特征选择-Fast PCA method of calculation of effective dimension reduction and other operations, and feature selection Platform: |
Size: 1024 |
Author:anan |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition Platform: |
Size: 2048 |
Author:sofi |
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Description: 用于特征降维人脸识别等多元数据分析的主分量分析投影的Matlab代码实现。-For feature reduction and other multivariate data analysis, face recognition principal component analysis projection of the Matlab code implementation. Platform: |
Size: 1024 |
Author:moxibingdao |
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Description: sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper:
Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems. Engineering Applications of Artificial Intelligence, 13(3):263-278, 2000.
rsar reads in a MIMO (Multiple Input, Multiple Output) dataset, performs RS-based feature selection on it, and returns the selected feature subset.
Four versions of the QuickReduct algorithm are supported, QuickReduct, QuickReduct III, QuickReduct IV and QuickReduct V (progressively faster implementations). QuickReduct II is a backward elimination version of QuickReduct and is not supported yet neither is exhaustive search for reducts. -sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper:
Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems. Engineering Applications of Artificial Intelligence, 13(3):263-278, 2000.
rsar reads in a MIMO (Multiple Input, Multiple Output) dataset, performs RS-based feature selection on it, and returns the selected feature subset.
Four versions of the QuickReduct algorithm are supported, QuickReduct, QuickReduct III, QuickReduct IV and QuickReduct V (progressively faster implementations). QuickReduct II is a backward elimination version of QuickReduct and is not supported yet neither is exhaustive search for reducts. Platform: |
Size: 730112 |
Author:NH |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
Size: 21504 |
Author:mhm |
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Description: 粗糙集代码
data reduction with fuzzy rough sets or fuzzy mutual information
fuzzy preference rough set based feature evaluation and selection
-Rough code data reduction with fuzzy rough sets or fuzzy mutual information fuzzy preference rough set based feature evaluation and selection Platform: |
Size: 38912 |
Author:gq |
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Search - feature reduction