Description: 基于样本和主轴核函数的相似度的动态聚类算法程序-based on samples and nuclear spindle function of the similarity dynamic clustering algorithm procedures Platform: |
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Author:alex |
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Description: 里面有模糊聚类分类方法FCM的代码和数字水印算法LSB的实现代码!-There are fuzzy clustering of FCM classification code and digital watermarking algorithm LSB realization of code! Platform: |
Size: 1024 |
Author:代松 |
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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 Platform: |
Size: 4271104 |
Author:查日东 |
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Description: 模糊核聚类算法的几篇论文及matlab源码，可以以练代学，更好掌握模糊聚类方法。-Fuzzy Kernel Clustering Algorithm matlab several papers and source code, can be practicing on behalf of science, to better grasp the fuzzy clustering method. Platform: |
Size: 1380352 |
Author:大长今 |
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Description: 介绍了一种非常实用的特征提取新方法，针对稀疏核主成分分析方法在特征提取中的不足, 提出了一种基于核K- 均值聚类的稀疏核主成分分析( Sparse KPCA) 的特征提取方法用于说话人识别。-Introduced a very useful new method of feature extraction for Sparse Kernel Principal Component Analysis in Feature Extraction of the lack of a kernel-based K-means clustering of sparse kernel principal component analysis (Sparse KPCA) of the feature extraction methods for speaker recognition. Platform: |
Size: 122880 |
Author:毋桂萍 |
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Description: kfcm，为模糊核聚类算法，用于将低维的数据映射到高维进行分类，是较先进的算法-kfcm, the fuzzy kernel clustering algorithm for low-dimensional data is mapped to high-dimensional classification, is a more advanced algorithms Platform: |
Size: 1380352 |
Author:wang |
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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. Platform: |
Size: 3072 |
Author:johhnny |
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Description: 基于核的FCM，可以用于聚类或者图像分割，但运行速度不是很快 自己不知道如何优化-Kernel-based FCM, can be used for clustering or image segmentation, but the speed is not fast they do not know how to optimize Platform: |
Size: 1024 |
Author:盛春冬 |
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Description: 拉普拉斯特征映射，采用热核构造权重，是一种基于流行学习的非线性降维技术，可用于图像分割提高聚类的性能-Laplacian Eigenmap is a kind of nonlinear dimensionality reduction technique which based on manifold study, it choose the weights W using the heat kernel and it can be used for image segmentation to promote clustering performance. Platform: |
Size: 1024 |
Author:QSJ |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。-In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms, and two and a support vector machine decision tree classification algorithm of moving objects. Platform: |
Size: 544768 |
Author:piano |
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Description: 机器学习matlab源代码，包括多分类SVM，模式识别，特征选择，回归等算法。-The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with, e.g model selection, statistical tests and visual plots.
Core library objects
wilcoxon
Wilcoxon object- significance test of results
joint_kernel
Joint kernel object- for calculating inner products in joint feature spaces
joint_data
Joint_data object
corrt_test
corrected resampled t-test object- significance test of results
Unsupervised objects
spectral
Spectral clustering
ppca
Probabilistic Principal Components Analysis
nmf
Non-negative Matrix Factorization object
mrank
Manifold Ranking
Density Estimation objects
indep
Feature selection by independent density estimation
Feature Selection objects
r2w2_sel
Feature scaling/selection via SVMs and r^2/w^2 bound.
nfe
Non-Linear Feature Elimination
mutin Platform: |
Size: 212992 |
Author:刘阳 |
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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.) Platform: |
Size: 8192 |
Author:daxingxing001
|
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Search - kernel clustering matlab