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Recently, graph-based semi-supervised learning (SSL) becomes a hot topic in machine learning and pattern recognition. It has been shown that constructing an informative graph is one of the most important steps in SSL since a good graph can significantly affect the final performance of learning algorithms. This paper has the following main contributions. First, we introduce a new graph construction...
Local structures and global structures of data sets are both important information for learning from highly nonlinear data. However, existing manifold learning algorithms either neglect one of them or have limitation on describing them. In this paper, we proposed a new two-step framework that fusing the global and local information to unfold highly nonlinear data. It first learns the global structures...
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