Semi-supervised Laplacian Eigenmap
Zhou Cong-hui · Jisuanji gongcheng yu sheji · 2012
How incorporate manifold learning and semi-supervised machine learning to extend the manifold learning algorithm.One way is to use the prior information in the form of on-manifold coordinates of certain data samples to compute the low-dimension coordinates of the other data samples.Combined Laplacian Eigenmap(LE) with semi-supervised machine learning,a semi-supervised Laplacian Eigenmap(SSLE) is presented.Simulation and real examples show that SSLE is more effective in clasaification and recognition field.