Metric Learning for Multi-Output Tasks

Liu, W; Xu, D; Tsang, IW; Zhang, W

Metric Learning for Multi-Output Tasks

Liu, W

Xu, D

Tsang, IW

Zhang, W

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41 (2), pp. 408 - 422
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Liu, W https://orcid.org/0000-0003-2450-3369	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-4453-7056	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Zhang, W	en_US
dc.date.accessioned	2020-04-21T05:39:13Z
dc.date.available	2020-04-21T05:39:13Z
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41 (2), pp. 408 - 422	en_US
dc.identifier.issn	0162-8828	en_US
dc.identifier.uri	http://hdl.handle.net/10453/140148
dc.description.abstract	© 1979-2012 IEEE. Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor (k \text{NN}) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of k \text{NN} depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.	en_US
dc.relation.ispartof	IEEE Transactions on Pattern Analysis and Machine Intelligence	en_US
dc.relation.isbasedon	10.1109/TPAMI.2018.2794976	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Metric Learning for Multi-Output Tasks	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	41	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	41	en_US

Abstract:

© 1979-2012 IEEE. Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor (k \text{NN}) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of k \text{NN} depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.

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http://hdl.handle.net/10453/140148