Unsupervised Many-to-Many Object Matching for Relational Data

被引：7

作者：

Iwata, Tomoharu ^{[1
]}

Lloyd, James Robert ^{[2
]}

Ghahramani, Zoubin ^{[2
]}

机构：

[1] NTT Commun Sci Labs, Kyoto, Japan

[2] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2016年 / 38卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

Unsupervised object matching; Bayesian nonparametrics; relational data; stochastic block model; MCMC;

D O I：

10.1109/TPAMI.2015.2469284

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a method for unsupervised many-to-many object 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.

引用

页码：607 / 617

页数：11