A semi-supervised clustering algorithm based on local scaling graph and label propagation
A semi-supervised clustering algorithm is proposed based on local scaling graph and label propagation. The main idea of this algorithm is that those samples locating in a local neighborhood share the same labels and the global labels changing among the graph is sufficiently smooth. The algorithm firstly introduces a local scaling graph to describe neighborhood among all the samples. Then an objective function and a constraint equation are proposed, which stand for the global smoothness of the category labels changing and the semi-supervised information respectively. Finally, the clustering task can be expressed by a typical quadratic program, whose optimal solution can minimize the overall smoothness of the labels changing and satisfy the constraint Experimental results of the algorithm on toy data, digit recognition, and text clustering demonstrate the feasibility and efficiency of the proposed algorithm.
semi-supervised learning K-nearest neighbor graph quadratic program classification
Jiani Hu
School of information and telecommunication Beijing University of Posts and Telecommunications Beijing, China
国际会议
哈尔滨
英文
1059-1062
2011-12-24（万方平台首次上网日期，不代表论文的发表时间）