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Exploiting side information in distance dependent Chinese restaurant processes for data clustering

conference contribution
posted on 2013-01-01, 00:00 authored by Cheng Li, Quoc-Dinh Phung, Santu RanaSantu Rana, Svetha VenkateshSvetha Venkatesh
Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.



Multimedia and Expo. IEEE International Conference (14th : 2013 : San Jose, California)


1 - 6




San Jose, California

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo