2010年
Variational Bayes learning over multiple graphs
Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
- ,
- 開始ページ
- 166
- 終了ページ
- 171
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/MLSP.2010.5589257
Learning (or mining) patterns in graphs has become an important issue in a lot of applications, including web, text and biology. Our issue is graph clustering, i.e. clustering nodes (examples) in a given network. We deal with a situation that we have multiple graphs, sharing nodes but having different edges, where each graph can have only part of the entire true clusters which we call localized clusters, being found in only part of all given graphs. For this issue, we present a probabilistic generative model and its robust learning scheme, being based on variational Bayes estimation. We empirically demonstrate the effectiveness of the proposed framework by using synthetic and real graphs. ©2010 IEEE.
- ID情報
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- DOI : 10.1109/MLSP.2010.5589257
- SCOPUS ID : 78449299703