Training boosting-like algorithms with semi-supervised subspace learning

Xu, J; Wu, Q; Zhang, J; Shen, F; Tang, Z

Training boosting-like algorithms with semi-supervised subspace learning

Xu, J Wu, Q

Zhang, J

Shen, F Tang, Z

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 4302 - 4306
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Xu, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Shen, F	en_US
dc.contributor.author	Tang, Z	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 4302 - 4306	en_US
dc.identifier.isbn	9781479923410	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28026
dc.description.abstract	Boosting algorithms have attracted great attention since the first real-time face detector by Viola & Jones through feature selection and strong classifier learning simultaneously. On the other hand, researchers have proposed to decouple such two procedures to improve the performance of Boosting algorithms. Motivated by this, we propose a boosting-like algorithm framework by embedding semi-supervised subspace learning methods. It selects weak classifiers based on class-separability. Combination weights of selected weak classifiers can be obtained by subspace learning. Three typical algorithms are proposed under this framework and evaluated on public data sets. As shown by our experimental results, the proposed methods obtain superior performances over their supervised counterparts and AdaBoost. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICIP.2013.6738886	en_US
dc.title	Training boosting-like algorithms with semi-supervised subspace learning	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Boosting algorithms have attracted great attention since the first real-time face detector by Viola & Jones through feature selection and strong classifier learning simultaneously. On the other hand, researchers have proposed to decouple such two procedures to improve the performance of Boosting algorithms. Motivated by this, we propose a boosting-like algorithm framework by embedding semi-supervised subspace learning methods. It selects weak classifiers based on class-separability. Combination weights of selected weak classifiers can be obtained by subspace learning. Three typical algorithms are proposed under this framework and evaluated on public data sets. As shown by our experimental results, the proposed methods obtain superior performances over their supervised counterparts and AdaBoost. © 2013 IEEE.

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http://hdl.handle.net/10453/28026