Sublinear dual coordinate ascent for regularized loss minimization

Liu, L; Tao, D

Sublinear dual coordinate ascent for regularized loss minimization

Liu, L Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2017, pp. 1065 - 1070
Issue Date:: 2017-01-31

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Field	Value	Language
dc.contributor.author	Liu, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2017-01-31	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2017, pp. 1065 - 1070	en_US
dc.identifier.isbn	9781509054725	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126799
dc.description.abstract	© 2016 IEEE. We present a sublinear version of the dual coordinate ascent method for solving a group of regularized loss minimization problems in machine learning. The proposed method seamlessly integrates sampling techniques, the dual coordinate ascent method, and a multiplicative update algorithm. The sampling techniques choose the "expected" examples, and estimate the corresponding inner products. The dual coordinate ascent method generates an updated iterative step, which outperforms the time-learning step used in the previous sublinear perceptron algorithm. The multiplicative update algorithm updates the example weighting. The proposed method is implemented with an iterative step of order O(log(n)), where n is the size of examples, and achieves a better result than other methods, with high probability. We present a theoretical analysis of the sublinear iterative in order to justify its benefits. We then apply the proposed optimization method to support vector machine and conduct experiments on three large-scale datasets. Our experimental results validate our theoretical findings.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2016.79	en_US
dc.title	Sublinear dual coordinate ascent for regularized loss minimization	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. We present a sublinear version of the dual coordinate ascent method for solving a group of regularized loss minimization problems in machine learning. The proposed method seamlessly integrates sampling techniques, the dual coordinate ascent method, and a multiplicative update algorithm. The sampling techniques choose the "expected" examples, and estimate the corresponding inner products. The dual coordinate ascent method generates an updated iterative step, which outperforms the time-learning step used in the previous sublinear perceptron algorithm. The multiplicative update algorithm updates the example weighting. The proposed method is implemented with an iterative step of order O(log(n)), where n is the size of examples, and achieves a better result than other methods, with high probability. We present a theoretical analysis of the sublinear iterative in order to justify its benefits. We then apply the proposed optimization method to support vector machine and conduct experiments on three large-scale datasets. Our experimental results validate our theoretical findings.

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http://hdl.handle.net/10453/126799