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Description: 首先对原始数据 归一化
然后进行PCA分析
采用PCs作为特征，
进行模糊聚类分析 fuzzy c-means method-First normalized the raw data and then proceed to PCA analysis using PCs as features, fuzzy clustering analysis for fuzzy c-means method
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Author: 王一 |
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Description: 利用PCA和模糊C均值相结合的方法实现图象的聚类-Using PCA and fuzzy C-means method of combining images to achieve the clustering
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Author: 王英 |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm
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Author: 查日东 |
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Description: 提出一种基于主分量分析和相融性度量的快速聚类方法。通过构造主分量空间将高维数据投影到两个主成分上
进行特征提取，每一个主分量都是原始变量的线性组合-Is proposed based on Principal Component Analysis and Measure of blending fast clustering method. Principal component space by constructing a high-dimensional data onto two principal component on feature extraction, each principal component is a linear combination of original variables
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Author: f0700 |
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Description: Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.
We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.
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Author: johhnny |
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Description: pca-kmeans聚类
先将数据(wine,uci数据集)降维处理，在进行聚类-pca-kmeans clustering
use the data of uci:wine.
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Size: 7168 |
Author: 王 |
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Description: This file implement PCA algorithm for dimension reduction and clustering techniques.
Principle Component Analysis, Dimension reduction, Clustering.
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Author: matin |
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Description: labview关于机器学习的案例，来源于NI lab，有多种方法：BP、Kernel PCA、Clustering、svm等等，值得大家学习。-labview on the case of machine learning, from the NI lab, there are a number of ways: BP, the Kernel the PCA, Clustering, svm, etc., is worth learning.
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Size: 338944 |
Author: awen |
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Description: pca his code to apply PCA (Principal Component Analysis)
for any information please send to engalaatharwat@hotmail.com
Egypt - HICIT - +20106091638
-pca his code to apply PCA (Principal Component Analysis)
for any information please send to engalaatharwat@hotmail.com
Egypt - HICIT - +20106091638
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Size: 1226752 |
Author: Anusha |
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Description: 基于PCA有监督kohonen网络的网络入侵聚类。里面包含有原代码和说明文件。-Based on PCA supervised kohonen network clustering network intrusion
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Size: 93184 |
Author: Winjade |
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Description: pca主成分分析，在多变量选择上效果较好，对数据的主成分进行分析，常用于分类、聚类、实验数据处理-Pca principal component analysis in multivariate selection effect is good, principal component analysis of data, often used in classification, clustering, experimental data processing
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Size: 3072 |
Author: my |
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Description: pca算法原理介绍和仿真代码，主要用于数据的聚类，代码时用于图像上的聚类过程，聚类效果很好，就是有点慢-pca algorithm introduces the principle and simulation code, mainly for clustering data, a clustering process images on-time code, clustering works well, is a bit slow
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Author: 胡鹏程 |
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Description: 此代码为matlab代码，分为两个部分。第一部分实现K均值聚类算法应用它来压缩图像。在第二部分中，你将使用主成份分析法pca来实现人脸图像的低维表示。
-This code for the matlab code, is divided into two parts. The first part of the implementation of the K means clustering algorithm to compress the image. In the second part, you will use the principal component analysis method PCA to realize the low dimensional representation of the face image.
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Author: pudnkobe |
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Description: 主成分分析（Principal Component Analysis，PCA）， 是一种统计方法。通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量，转换后的这组变量叫主成分。-Principal component analysis is a statistical method. A set of variables that may be related to a set of variables that may be correlated to a set of variables that are not linearly dependent, after conversion is converted to a set of variables called principal components. This code is mainly used for process monitoring, fault diagnosis and other fields.
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Size: 4096 |
Author: ZhangTK |
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Description: 借鉴了主成分分析算法（PCA），基于欧几里得距离的聚类分析，music高阶谱分析算法。- It draws on principal component analysis algorithm (PCA), Clustering analysis based on Euclidean distance, music higher order spectral analysis algorithm.
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Size: 8192 |
Author: nensenfao |
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Description: K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the characteristics of data clustering data. The principal component analysis reduces the dimension, carries on the visualization operation to the data, reduces the dimension to the features.)
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Size: 33792 |
Author: 赵嘉慧
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Description: Andrew Ng Cousera 机器学习K-means勇于图像压缩 以及主成分分析PCA用在人脸识别，源代码以及说明文档。(Andrew Ng Cousera machine learning , the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images.)
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Author: mark198033
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Description: 降维，用作聚类算法使用。具有很好效果，可以用作图像去噪(Dimensionality reduction is used as a clustering algorithm. It has good effect and can be used for image denoising.)
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Size: 2048 |
Author: 王琳琳 |
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Description: 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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Author: lanji |
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Description: 该算法主要包含PCA算法和K-Means聚类算法，用于SAR变化检测,包含数据图片。(The algorithm mainly includes PCA algorithm and K-means clustering algorithm for SAR change detection, including data images.)
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Size: 240640 |
Author: 墨辞 |
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