Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization

Wang, M; Fu, W; Hao, S; Tao, D; Wu, X

Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization

Wang, M Fu, W Hao, S Tao, D

Wu, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2016, 28 (7), pp. 1864 - 1877
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Wang, M	en_US
dc.contributor.author	Fu, W	en_US
dc.contributor.author	Hao, S	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Wu, X	en_US
dc.date.issued	2016-07-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2016, 28 (7), pp. 1864 - 1877	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122794
dc.description.abstract	© 1989-2012 IEEE. Many graph-based semi-supervised learning methods for large datasets have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). This model builds a regularization framework by exploring the underlying structure of the whole dataset with both datapoints and anchors. Nevertheless, AGR still has limitations in its two components: (1) in anchor graph construction, the estimation of the local weights between each datapoint and its neighboring anchors could be biased and relatively slow; and (2) in anchor graph regularization, the adjacency matrix that estimates the relationship between datapoints, is not sufficiently effective. In this paper, we develop an Efficient Anchor Graph Regularization (EAGR) by tackling these issues. First, we propose a fast local anchor embedding method, which reformulates the optimization of local weights and obtains an analytical solution. We show that this method better reconstructs datapoints with anchors and speeds up the optimizing process. Second, we propose a new adjacency matrix among anchors by considering the commonly linked datapoints, which leads to a more effective normalized graph Laplacian over anchors. We show that, with the novel local weight estimation and normalized graph Laplacian, EAGR is able to achieve better classification accuracy with much less computational costs. Experimental results on several publicly available datasets demonstrate the effectiveness of our approach.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140102164
dc.relation	http://purl.org/au-research/grants/arc/FT130101457
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2016.2535367	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	28	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	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
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 1989-2012 IEEE. Many graph-based semi-supervised learning methods for large datasets have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). This model builds a regularization framework by exploring the underlying structure of the whole dataset with both datapoints and anchors. Nevertheless, AGR still has limitations in its two components: (1) in anchor graph construction, the estimation of the local weights between each datapoint and its neighboring anchors could be biased and relatively slow; and (2) in anchor graph regularization, the adjacency matrix that estimates the relationship between datapoints, is not sufficiently effective. In this paper, we develop an Efficient Anchor Graph Regularization (EAGR) by tackling these issues. First, we propose a fast local anchor embedding method, which reformulates the optimization of local weights and obtains an analytical solution. We show that this method better reconstructs datapoints with anchors and speeds up the optimizing process. Second, we propose a new adjacency matrix among anchors by considering the commonly linked datapoints, which leads to a more effective normalized graph Laplacian over anchors. We show that, with the novel local weight estimation and normalized graph Laplacian, EAGR is able to achieve better classification accuracy with much less computational costs. Experimental results on several publicly available datasets demonstrate the effectiveness of our approach.

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http://hdl.handle.net/10453/122794