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论文类型： 会议论文

发表时间： 2010-12-14

收录刊物： EI、Scopus

页面范围： 1181-1186

摘要： Graph-based semi-supervised learning algorithms have attracted a lot of attention. Constructing a good graph is playing an essential role for all these algorithms. Many existing graph construction methods(e.g. Gaussian Kernel etc.) require user input parameter, which is hard to configure manually. In this paper, we propose a parameter-free similarity measure Adaptive Similarity Estimation (ASE), which constructs the graph by adaptively optimizing linear combination of its neighbors. Experimental results show the effectiveness of our proposed method. ? 2010 IEEE.