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Description: 一种高级流形学习算法介绍,Hessian Eigenmaps,这种算法最先在这篇文章上绍,是流形学习算法的经典文章。
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Size: 337589 |
Author: 罗朝辉 |
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Description: 一种高级流形学习算法介绍,Hessian Eigenmaps,这种算法最先在这篇文章上绍,是流形学习算法的经典文章。-An advanced introduction manifold learning algorithm, Hessian Eigenmaps, this algorithm on the first article in this Shaozeng, manifold learning algorithm is a classic article.
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Size: 336896 |
Author: 罗朝辉 |
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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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Size: 1108992 |
Author: yang |
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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
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Size: 226304 |
Author: tra ba huy |
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Description: Laplacian Eigenmaps [10] uses spectral techniques to perform dimensionality reduction. This technique relies on the basic assumption that the data lies in a low dimensional manifold in a high dimensional space.[11] This algorithm cannot embed out of sample points, but techniques based on Reproducing kernel Hilbert space regularization exist for adding this capability.[12] Such techniques can be applied to other nonlinear dimensionality reduction algorithms as well.
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Size: 2048 |
Author: Karthikeyan |
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Description: 该工具箱中包含了多种降维算法。其中有传统的PCA和Local PCA算法,也有典型的流形学习算法,如Isomap、LLE、HLLE、Laplacian Eigenmaps 和 Local Tangent Space 。-The toolbox contains a variety of dimensionality reduction algorithms. In which the traditional PCA and Local PCA algorithms, there are the typical manifold learning algorithms such as Isomap, LLE, HLLE, Laplacian Eigenmaps and Local Tangent Space.
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Size: 195584 |
Author: 芝麻 |
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Description: Matlab针对各种数据预处理的降维方法,源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques:
Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Sammon mapping
Linear Discriminant Analysis (LDA)
Multidimensional scaling (MDS)
Isomap
Landmark Isomap
Local Linear Embedding (LLE)
Laplacian Eigenmaps
Hessian LLE
Local Tangent Space Alignment (LTSA)
Conformal Eigenmaps (extension of LLE)
Maximum Variance Unfolding (extension of LLE)
Landmark MVU (LandmarkMVU)
Fast Maximum Variance Unfolding (FastMVU)
Kernel PCA
Generalized Discriminant Analysis (GDA)
Diffusion maps
Stochastic Neighbor Embedding (SNE)
Symmetric SNE (SymSNE)
new: t-Distributed Stochastic Neighbor Embedding (t-SNE)
Neighborhood Preserving Embedding (NPE)
Locality Preserving Projection (LPP)
Linear Local Tangent Space Alignment (LLTSA)
Stochastic Proximity Embedding (SPE)
Mu
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Size: 2029568 |
Author: jdzsj |
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Description: 用于降维的matlab工具包,包括PCA,LDA,LLE,等-Matlab Toolbox for Dimensionality Reduction
Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Classical multidimensional scaling (MDS)
Sammon mapping
Linear Discriminant Analysis (LDA)
Isomap
Landmark Isomap
Local Linear Embedding (LLE)
Laplacian Eigenmaps
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Size: 1119232 |
Author: 晗嫣 |
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Description: laplacian eigenmaps算法-laplacian eigenmaps Algorithm
Platform: |
Size: 2048 |
Author: 刘恺楠 |
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Description: Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Sammon mapping
Linear Discriminant Analysis (LDA)
Multidimensional scaling (MDS)
Isomap
Landmark Isomap
Local Linear Embedding (LLE)
Laplacian Eigenmaps
Hessian LLE
Local Tangent Space Alignment (LTSA)
Conformal Eigenmaps (extension of LLE)
Maximum Variance Unfolding (extension of LLE)
Landmark MVU (LandmarkMVU)
Fast Maximum Variance Unfolding (FastMVU)
Platform: |
Size: 1003143 |
Author: 401116575@qq.com |
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