Method of Kernel-based Semi-supervised Local Linear Embedding
Xin Yang · Jisuanji gongcheng · 2011
In order to solve the defects that Locally Linear Embedding(LLE) can not make full use of lable information in unsupervised machine learning,this paper proposes a kind of semi-supervised kernel-based local linear embedding algorithm.Taking into account measure of Euclidean distance is easy to destroy the manifold,this algorithm maps the raw data into a high dimensional kernel space,uses the distances in high-dimensional space instead of euclidean distance,and introduces the thought of semi-supervised learning to adjust the distance with label information,which is used for linear reconstruction and dimension reduction,and enhances the ability on dimension reduction.In the standard data sets,face and character database,experimental results show the recognition rate of novel method is 2% higher than the traditional local linear embedding algorithms.