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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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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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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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流形学习程序，Isomap，LLE，LTSA，etc，非线性数据降维-manifold learning, Isomap, LLE, LTSA, etc, nonlinear data dimensionality reduction
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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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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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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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非线性降维的经典算法Isomap，还有人连数据可以做实验-The classic nonlinear dimensionality reduction algorithm Isomap, even the data can experiment
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非线性降维的经典算法Isomap，LLe,还有人连数据可以做实验-The classic nonlinear dimensionality reduction algorithm Isomap, even the data can experiment
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Isomap是一种非线性降维方法。又是一种广泛使用的低维嵌入的方法。[ 1 ] Isomap用于计算准等距，低维嵌入的一组高维数据点。该算法提供了一个简单的方法，用于估计基于一个粗略的估计，每个数据点的邻居流形上的数据流形的内在几何。Isomap是高效和一般适用于范围广泛的数据源和维度。-Isomap is a Nonlinear dimensionality reduction method. And is also one of several widely used low-dimensi
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自然杂志上关于流形学习的重要文章，用于非线性维数约简。
-Nature of manifold learning important articles, for nonlinear dimensionality reduction.
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基于核函数的非线性维数约简方法有基于核函数的主成分分(KPCA),本算法主要应用于过程监测、故障诊断等领域。-Kernel function based nonlinear dimensionality reduction method is based on kernel function (KPCA), which is mainly used in process monitoring, fault diagnosis and so on.
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一种流形学习算法，用于非线性降维算法，实验数据用的人脸数据。(A manifold learning algorithm for nonlinear dimensionality reduction algorithms, using face data for experimental data.)
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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 program is one of the typical nonlinear dimensionality reduction algorithms-LLE algorithm. Friends who want to study the dimensionality reduction of high-dimensional data are worth a look.)
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