Unsupervised and Semi-Supervised Extraction of Clusters from Hypergraphs(Pattern Recognition)
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概要
- 論文の詳細を見る
We extend a graph spectral method for extracting clusters from graphs representing pairwise similarity between data to hypergraph data with hyperedges denoting higher order similarity between data. Our method is robust to noisy outlier data and the number of clusters can be easily determined. The unsupervised method extracts clusters sequentially in the order of the majority of clusters. We derive from the unsupervised algorithm a semi-supervised one which can extract any cluster irrespective of its majority. The performance of those methods is exemplified with synthetic toy data and real image data.
- 社団法人電子情報通信学会の論文
- 2006-07-01
著者
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Inoue Kohei
Faculty Of Design Kyushu University
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Urahama Kiichi
Faculty Of Design Kyushu University
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DU Weiwei
Faculty of Design, Kyushu University
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Urahama Kiichi
Faculty Of Computer Science And Systems Engineering Kyushu Institute Of Technology
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Du Weiwei
Faculty Of Design Kyushu University
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