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

人脸识别的LPP方法的源代码，保局投影(LPP)作为拉普拉斯特征映射的一种线性逼近可以较好的反映样本的流形结构-LPP method for face recognition source code, locality preserving projection (LPP) manifold structure as a linear Laplasse feature mapping approach can better reflect the sample

拉普拉斯特征映射算法，可实现高维信号降维或实现带内滤波降噪。-Laplace feature mapping algorithm, can achieve high dimensional signal dimensionality reduction or implement band noise filtering.

拉普拉斯特征映射,最大差异展开，时频域特征(Laplacian Eigenmap Maximum difference expansion Fast Maximum difference expansion ISOMAP)

流形学习（Manifold Learning）的一种算法：拉普拉斯特征映射（LE）(An algorithm of Manifold Learning: Laplacian Feature Mapping (LE))