拉普拉斯降维方法，是非线性数据降维方法，通过构建相似关系图来重构数据局部流形结构特征。-Laplace dimension reduction method is non-linear data dimensionality reduction method, by constructing a graph similar to reconstruct the structure of local manifoldof the data.

此代码是关于流形学习，数据降维，代码中含有的主要方法是PCA，KPCA，MDS，KMDS，Laplacian等等，且代码作了可视化处理，界面效果完美-This code is on the manifold learning, data dimensionality reduction, the main method code is contained in PCA, KPCA, MDS, KMDS, Laplacian, etc., and the code visualization ma

对于CK信号进行降维，同时也有线性回归方法，里面有一个例子供大家学习-CK signal for dimensionality reduction, but also a linear regression method, which has an example for everyone to learn

LWPR 局部加权投影回归算法，是一种高效的数据降维方法，也能用于预测方法研究。 -LWPR is an effective dimensionality reduction approach. It can be used for prediction.

该压缩文件为部分数据降维方法，有LTSA、HHLLE、ISOMAP、LLTSA、LLP-The compressed file for the partial data dimensionality reduction method, there are LTSA, HHLLE, ISOMAP, LLTSA, LLP