Description: Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107. Platform: |
Size: 31609 |
Author:修宇 |
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Description: Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.-Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107. Platform: |
Size: 31744 |
Author:修宇 |
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Description: 此程序为非线性降维典型算法之一－－LLE算法，对想进行高维数据降维研究的朋友们值得一看-This procedure for nonlinear dimensionality reduction algorithm, one of the typical- LLE algorithm, to want to high-dimensional data dimensionality reduction study to see friends Platform: |
Size: 9216 |
Author:ttt |
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Description: 这是LLE的原始算法，原文的参考文献是：S.T.Roweis and L.K.Saul. Nonlinear dimensionality reduction by locally linear embedding. Science,
290, 2000.-This is the original LLE algorithm, the original reference is: STRoweis and LKSaul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2000. Platform: |
Size: 1024 |
Author:treat |
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Description: 关于高维数据降维的非线性方法ＬＬＥ代码，对学习数据降维有帮助-High dimensional data on the nonlinear dimensionality reduction methods LLE code, data dimensionality reduction in learning help Platform: |
Size: 3072 |
Author:hyj_math |
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Description: 一种流形学习算法，用于非线性降维，文章发表在2000年science杂志上，是一种非常经典的算法。-A manifold learning algorithm for nonlinear dimensionality reduction, articles published in science journal in 2000, is a very classic algorithms. Platform: |
Size: 2048 |
Author:仲国强 |
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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 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: 一类非线性EV模型的降维估计Nonlinear dimensionality reduction model is estimated EV-Nonlinear dimensionality reduction model is estimated EV Platform: |
Size: 333824 |
Author:ku |
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