Robust top-k multiclass SVM for visual category recognition

Chang, X; Yu, YL; Yang, Y

Robust top-k multiclass SVM for visual category recognition

Chang, X

Yu, YL Yang, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, Part F129685 pp. 75 - 83
Issue Date:: 2017-08-13

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Field	Value	Language
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807	en_US
dc.contributor.author	Yu, YL	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.date.issued	2017-08-13	en_US
dc.identifier.citation	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, Part F129685 pp. 75 - 83	en_US
dc.identifier.isbn	9781450348874	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126442
dc.description.abstract	© 2017 Association for Computing Machinery. Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow the learning algorithm to make mistakes on similar classes, as long as the true class is still among the top-k (say) predictions. Likewise, in applications such as search engine or ad display, we are allowed to present k predictions at a time and the customer would be satisfied as long as her interested prediction is included. Inspired by the recent work of [15], we propose a very generic, robust multiclass SVM formulation that directly aims at minimizing a weighted and truncated combination of the ordered prediction scores. Our method includes many previous works as special cases. Computationally, using the Jordan decomposition Lemma we show how to rewrite our objective as the difference of two convex functions, based on which we develop an eficient algorithm that allows incorporating many popular regularizers (such as the l2 and l1 norms). We conduct extensive experiments on four real large-scale visual category recognition datasets, and obtain very promising performances.	en_US
dc.relation.ispartof	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining	en_US
dc.relation.isbasedon	10.1145/3097983.3097991	en_US
dc.title	Robust top-k multiclass SVM for visual category recognition	en_US
dc.type	Conference Proceeding
utslib.citation.volume	Part F129685	en_US
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/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	Part F129685	en_US

Abstract:

© 2017 Association for Computing Machinery. Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow the learning algorithm to make mistakes on similar classes, as long as the true class is still among the top-k (say) predictions. Likewise, in applications such as search engine or ad display, we are allowed to present k predictions at a time and the customer would be satisfied as long as her interested prediction is included. Inspired by the recent work of [15], we propose a very generic, robust multiclass SVM formulation that directly aims at minimizing a weighted and truncated combination of the ordered prediction scores. Our method includes many previous works as special cases. Computationally, using the Jordan decomposition Lemma we show how to rewrite our objective as the difference of two convex functions, based on which we develop an eficient algorithm that allows incorporating many popular regularizers (such as the l2 and l1 norms). We conduct extensive experiments on four real large-scale visual category recognition datasets, and obtain very promising performances.

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http://hdl.handle.net/10453/126442