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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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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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流形学习，局部线性嵌入式算法(LLE)，一种智能的算法去推测捕捉高维空间中所包含的低维特征。与适合于局部维数约减的聚类算法不同，LLE算法在单一的低维的全域坐标系统中表征采样空间，然而它并没有优化最小局域。通过对线性重构的局域对称的研究应用，LLE能够描述非线性流形的全局结构，例如那些人脸的数据集或者文本文档集-Manifold learning, embedded local linear algorithm (LLE), an intelligent algorithm to predict
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Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensiona
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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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该压缩包包含神经网络在MATLAB里的应用：
1、非线性函数拟合
2、RBF网络-非线性函数回归
3、粒子群算法非线性极值寻优
4、神经网络极值寻优
5、神经网络建模自变量降维
6、BP网络-非线性函数回归-The archive contains neural network in MATLAB: 1, non-linear function fitting 2, RBF network- 3 nonlinear function regression, nonline
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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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输入： 二维矩阵；输出：降维结果； 共包含34种降维方法，线性/非线性；局部/全局；监督/非监督(Input: 2-D matrix; output: dimension reduction result; contains 34 dimensionality reduction methods, linear / nonlinear; local / global; supervised / unsupervised.)
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