Presentation	2012-11-07 Subset Infinite Relational Models Katsuhiko ISHIGURO, Naonori UEDA, Hiroshi SAWADA,
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Abstract(in English)	We propose a new probabilistic generative model for analyzing sparse and noisy relational data, such as friend-links on social network services and customer records in online shops. Real-world relational data often include a large portion of non-informative data entries. Many existing stochastic blockmodels suffer from these irrelevant data entries. The proposed model incorporates a latent variable that explicitly indicates whether each data entry is relevant or not to diminish bad effects associated with such irrelevant data. Through experiments, we show that the proposed model can extract clusters with stronger relations among data within the cluster than clusters obtained by the conventional model.
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Keyword(in English)	clustering / relational data / nonparametric Bayes
Paper #	IBISML2012-37
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Conference Information
Committee	IBISML
Conference Date	2012/10/31(1days)
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Paper Information
Registration To	Information-Based Induction Sciences and Machine Learning (IBISML)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Subset Infinite Relational Models
Sub Title (in English)
Keyword(1)	clustering
Keyword(2)	relational data
Keyword(3)	nonparametric Bayes
1st Author's Name	Katsuhiko ISHIGURO
1st Author's Affiliation	NTT Corporation()
2nd Author's Name	Naonori UEDA
2nd Author's Affiliation	NTT Corporation
3rd Author's Name	Hiroshi SAWADA
3rd Author's Affiliation	NTT Corporation
Date	2012-11-07
Paper #	IBISML2012-37
Volume (vol)	vol.112
Number (no)	279
Page	pp.pp.-
#Pages	8
Date of Issue