ゲノム情報科学研究教育機構  アブストラクト
Date Nov 24, 2015
Speaker Dr. Tomoharu Iwata, Research Scientist, NTT Communication Science Laboratries, Japan
Title Unsupervised Cluster Matching for Relational Data
Abstract We propose a method for unsupervised cluster matching from multiple networks, which is the task of finding correspondences between groups of nodes in different networks. For example, the proposed method can discover shared word groups from multi-lingual document-word networks without cross-language alignment information. We assume that multiple networks share groups, and each group has its own interaction pattern with other groups. Using infinite relational models with this assumption, objects in different networks are clustered into common groups depending on their interaction patterns, discovering a matching. The effectiveness of the proposed method is experimentally demonstrated by using synthetic and real relational data sets, which include applications to cross-domain recommendation without shared user/item identifiers and multi-lingual word clustering.
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