CorrLog: Correlated logistic models for joint prediction of multiple labels

Bian, W; Xie, B; Tao, D

CorrLog: Correlated logistic models for joint prediction of multiple labels

Bian, W Xie, B Tao, D

Permalink

Publication Type:: Conference Proceeding
Citation:: Journal of Machine Learning Research, 2012, 22 pp. 109 - 117
Issue Date:: 2012-01-01

Closed Access

	Filename	Description	Size
	2011007227OK.pdf		858.99 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Xie, B	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Journal of Machine Learning Research, 2012, 22 pp. 109 - 117	en_US
dc.identifier.issn	1532-4435	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23498
dc.description.abstract	In this paper, we present a simple but effective method for multi-label classification (MLC), termed Correlated Logistic Models (Corrlog), which extends multiple Independent Logistic Regressions (ILRs) by modeling the pairwise correlation between labels. Algorithmically, we propose an efficient method for learning parameters of Corrlog, which is based on regularized maximum pseudolikelihood estimation and has a linear computational complexity with respect to the number of labels. Theoretically, we show that Corrlog enjoys a satisfying generalization bound which is independent of the number of labels. The effectiveness of Corrlog on modeling label correlations is illustrated by a toy example, and further experiments on real data show that Corrlog achieves competitive performance compared with popular MLC algorithms.	en_US
dc.relation.ispartof	Journal of Machine Learning Research	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	CorrLog: Correlated logistic models for joint prediction of multiple labels	en_US
dc.type	Conference Proceeding
utslib.citation.volume	22	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
dc.location.activity	La Palma, Canary Islands	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

In this paper, we present a simple but effective method for multi-label classification (MLC), termed Correlated Logistic Models (Corrlog), which extends multiple Independent Logistic Regressions (ILRs) by modeling the pairwise correlation between labels. Algorithmically, we propose an efficient method for learning parameters of Corrlog, which is based on regularized maximum pseudolikelihood estimation and has a linear computational complexity with respect to the number of labels. Theoretically, we show that Corrlog enjoys a satisfying generalization bound which is independent of the number of labels. The effectiveness of Corrlog on modeling label correlations is illustrated by a toy example, and further experiments on real data show that Corrlog achieves competitive performance compared with popular MLC algorithms.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/23498