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SUMMARY:Unsupervised Cluster Matching for Relational Data - Tomoharu Iwata
 \, NTT Communication Science Laboratories
DTSTART:20161102T110000Z
DTEND:20161102T120000Z
UID:TALK68832@talks.cam.ac.uk
CONTACT:44515
DESCRIPTION:We propose a method for unsupervised cluster matching from mul
 tiple networks\, which is the task of finding correspondences between grou
 ps of nodes in different networks. For example\, the proposed method can d
 iscover shared word groups from multi-lingual document-word networks witho
 ut cross-language alignment information. We assume that multiple networks 
 share groups\, and each group has its own interaction pattern with other g
 roups. Using infinite relational models with this assumption\, objects in 
 different networks are clustered into common groups depending on their int
 eraction patterns\, discovering a matching. The effectiveness of the propo
 sed method is experimentally demonstrated by using synthetic and real rela
 tional data sets\, which include applications to cross-domain recommendati
 on without shared user/item identifiers and multi-lingual word clustering.
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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