LLE原理

  流形学习的任务就是把这个高维流形映射到一个低维（例如2维）的空间里。流形学习可以分为线性算法和非线性算法，前者包括主成分分析（PCA）和线性判别分析（LDA），后者包括等距映射（Isomap），拉普拉斯特征映射（LE）等。(The task of manifold learning is to map this high-dimensional manifold into a low-dimensional (for example, 2-dimensional) space. Manifold learning can be divided into linear algorithms and nonlinear algorithms. The former includes principal component analysis (PCA) and linear discriminant analysis (LDA). The latter includes isometric mapping (Isomap) and Laplacian feature mapping (LE).)

LLE原理.doc, 244736 , 2017-11-27