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 Platform: |
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. Platform: |
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. Platform: |
Size: 195584 |
Author:芝麻 |
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