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Description: 基于kernel pca的非线性降维算法，原文发表于神经计算杂志上，有兴趣者可以先看论文。-PCA-based kernel of nonlinear reduced dimension algorithm, the original published in the Journal of neural computation, those interested can read papers.
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Author: 武旗 |
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Description: 马尔可夫随机场去噪的例子及其Kernel PCA-Markov Random Field Denoising examples and Kernel PCA
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Author: 王颖 |
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Description: kpca算法的matlab源程序,基于核的主成分分析的源程序-KPCA algorithm matlab source, kernel-based principal component analysis of the source
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Author: 曲伟 |
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Description: kernel PCA
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Author: wzh |
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Description: pca+fisher是将核函数应用到人脸识别研究中去-pca+ fisher is the kernel function is applied to face recognition research go
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Author: zhangwenming |
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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: 这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值- ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techniques are available:
- Principal Component Analysis ( PCA )
- Linear Discriminant Analysis ( LDA )
- Multidimensional scaling ( MDS )
- Probabilistic PCA ( ProbPCA )
- Factor analysis ( FactorAnalysis )
- Sammon mapping ( Sammon )
- Isomap ( Isomap )
- Landmark Isomap ( LandmarkIsomap )
- Locally Linear Embedding ( LLE )
- Laplacian Eigenmaps ( Laplacian )
- Hessian LLE ( HessianLLE )
- Local Tangent Space Alignment ( LTSA )
- Diffusion maps ( DiffusionMaps )
- Kernel PCA ( KernelPCA )
- Generalized Discriminant Analysis ( KernelLDA )
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Author: yang |
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Description: Kernel PCA的经典示例程序，非常有帮助，也很易懂。-Kernel PCA classic example of the procedure
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Author: 张扬 |
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Description: Palmprint Recognition by Applying Wavelet-Based Kernel PCA
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Author: 孙也 |
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Description: 基于核的主分量分析方法的提出者亲自写的程序（基于MATLAB-a MATLAB m-file of Kernel PCA
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Author: Steven |
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Description: This pdf file of Kernel PCA-This is pdf file of Kernel PCA
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Size: 134144 |
Author: Huy Tung |
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Description: Kernel PCA toy example Nonlinear component analysis as a kernel Eigenvalue problem
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Author: henry |
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Description: 关于rbf神经网络实现图像分类的优化算法英文文献 源于著名期刊I-A METHOD FOR IMAGE CLASSIFICATION BASED ON KERNEL PCA
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Author: sfes |
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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: --- --- --- --- ---Kernel PCA --- --- --- --- -
kpca1 | Simple Example of Kernel PCA on a artificial datasets
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kpca2 | Example of KPCA. this is a B.Scholkopf modified routine
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kpca3 | Example of Multilayer SVM with a KPCA as a first stage- ---------------------------Kernel PCA -------------------------
kpca1 | Simple Example of Kernel PCA on a artificial datasets
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kpca2 | Example of KPCA. this is a B.Scholkopf modified routine
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kpca3 | Example of Multilayer SVM with a KPCA as a first stage
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Author: saiful |
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Description: Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.
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Author: Karthikeyan |
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Description: face recognition using PCA
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Author: Waqar |
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Description: Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.-Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.
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Author: rem |
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Description: 基于核方法的主成分分析matlab源代码，比较经典，推荐学习。-Method based on kernel principal component analysis matlab source code, more classic, recommended learning.
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Size: 1024 |
Author: 石远超 |
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Description: 实现了主元分析（PCA）和独立分量分析（ICA）相关信号处理。非线性降维。(Implements Principal Component Analysis (PCA) and Independent Component Analysis (ICA) correlation signal. Non-linear dimension reduction using kernel PCA.)
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Author: SONAH~
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