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Descr iption: 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 Transactio
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LLE 一种非线性维数约减算法，非常好用-LLE a nonlinear dimensionality reduction algorithm, a very handy
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基于局部线性嵌入_LLE_非线性降维的多流形学习,是当前人脸识别的新方向,Face Recognition Based on Locally Linear Embedding for Nonlinear Dimensionality Reduction _LLE_ multi-manifold learning
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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 b
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非线性降维方法KPCA 可以应用于高维数据的机器学习-KPCA nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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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
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非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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非线性降维方法llc 可以应用于高维数据的机器学习-Llc nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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非线性降维方法lle 可以应用于高维数据的机器学习-Lle nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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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.
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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
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Nonlinear Dimensionality Reduction for Face Recognition.pdf
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一篇2000年流行的降维文章。学习降维必看。-Nonlinear Dimensionality Reduction by Locally Linear Embedding
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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
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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
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等距映射，一种用于非线性降维的全球性的几何框架，适用于学习内部平坦的低维流形-Isomap is a global geometric framework for nonlinear dimensionality
reduction,it suitable for learning low dimensional manifold which inside is flat
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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
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高光谱遥感影像数据降维处理 包括线性降维和非线性降维处理-Hyperspectral remote sensing image data to reduce the dimension of linear dimensionality reduction and nonlinear dimensionality reduction processing
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介绍一种非线性降维方法的文章，是篇算法介绍-A Global Geometric Framework
for Nonlinear Dimensionality
Reduction
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