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NPEmatlab源码
- LGE是NPE需要调用的算法
NPE.邻域保持嵌入算法
- 邻域保持嵌入算法。注释有对应的参考文献和使用说明！,Embedding Algorithm maintain neighborhood. Notes there is the corresponding references and the use of note!
NPE
- 本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embeddi
npe
- 流形学习算法lle的线性化方法，是一种非监督的降维方法，比lle的优势在于可以将新的样本点映射到低维空间。-Lle manifold learning algorithm of the linearization method, is a non-supervised dimensionality reduction method has the advantage of being more than lle can sample the new point is mapped to the
NPE
- NPE: Neighborhood Preserving Embedding
NPE
- 用于流型学习的算法：局部邻域保持 可以分析任何符合流型分布的数据集，常用语人脸识别-For the flow pattern learning algorithm: the local neighborhood to maintain consistent flow pattern can be analyzed the distribution of any data set, common language recognition
MATLABCodesforDimensionalityReduction
- 维数约减matlab工具箱，包括LLE，ISOMAP，NPE等，具有较好的效果-Dimensionality reduction matlab toolbox, including LLE, ISOMAP, NPE, etc., with good results
LGE
- 线性图嵌入方法，该方法是一种基于图框架的子空间学习方法，被用于LPP,NPE等流行学习方法中。-(Regularized) Linear Graph Embedding (Provides a general framework for graph based subspace learning. This function will be called by LPP, NPE, IsoProjection, LSDA, MMP ...)
NPE
- 流行学习的一种：邻域保持投影，该方法是局部线性嵌入的线性拓展，较好的保持了数据的局部线性结构-Neighborhood Preserving Embedding (You need to download LGE.m)
DRTOOL_drtoolbox
- matlab 降维工具箱，最新版本。包含各类线性及非线性降维代码，lle,lpp,mvu,isomap,npe等皆在其中。-DRTOOL, by itself, creates a new DRTOOL or raises the existing singleton*. H = DRTOOL returns the handle to a new DRTOOL or the handle to the existing single
Multiscale-NPE-FOR-fault-detection
- 首先对一段正常工况下的历史数据进行离散小波分解,对不同尺度下的小波系数建立相应的NPE模型.经过多层小波分解,建立相应的统计量对过程进行监控-First discrete wavelet decomposition of some normal conditions of historical data, the NPE model wavelet coefficients in different scales. Multilayer wavelet decomposition, the es
drtoolbox
- Matlab针对各种数据预处理的降维方法，源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques: Principal Component Analysis (PCA) Probabilistic PCA Factor Analysis (FA) Sammon mapping Lin