research-article

Learning instance specific distances using metric propagation

Authors:

Zhi-Hua ZhouAuthors Info & Claims

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

Pages 1225 - 1232

https://doi.org/10.1145/1553374.1553530

Published: 14 June 2009 Publication History

Abstract

In many real-world applications, such as image retrieval, it would be natural to measure the distances from one instance to others using instance specific distance which captures the distinctions from the perspective of the concerned instance. However, there is no complete framework for learning instance specific distances since existing methods are incapable of learning such distances for test instance and unlabeled data. In this paper, we propose the Isd method to address this issue. The key of Isd is metric propagation, that is, propagating and adapting metrics of individual labeled examples to individual unlabeled instances. We formulate the problem into a convex optimization framework and derive efficient solutions. Experiments show that Isd can effectively learn instance specific distances for labeled as well as unlabeled instances. The metric propagation scheme can also be used in other scenarios.

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Cited By

Guo XLi LDang CLiang JWei W(2023)Multiple metric learning via local metric fusionInformation Sciences10.1016/j.ins.2022.11.118621(341-353)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.118
Chen YZhao FZhang NZhou C(2021)Semi-supervised Subspace Metric Learning2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC)10.1109/ICFTIC54370.2021.9647276(66-76)Online publication date: 12-Nov-2021
https://doi.org/10.1109/ICFTIC54370.2021.9647276
Guo XDang CLiang JWei WLiang J(2021)Metric learning with clustering-based constraintsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-021-01408-3Online publication date: 25-Aug-2021
https://doi.org/10.1007/s13042-021-01408-3
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Learning instance specific distances using metric propagation

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Published In

cover image ACM Other conferences

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

June 2009

1331 pages

ISBN:9781605585161

DOI:10.1145/1553374

General Chair:
Andrea Danyluk
Williams College
,
Program Chairs:
Léon Bottou
NEC Laboratories America
,
Michael Littman
Rutgers University

Copyright © 2009 Copyright 2009 by the author(s)/owner(s).

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NSF
Microsoft Research: Microsoft Research
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

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ICML '09

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Microsoft Research

ICML '09: The 26th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 14 - 18, 2009

Quebec, Montreal, Canada

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Guo XLi LDang CLiang JWei W(2023)Multiple metric learning via local metric fusionInformation Sciences10.1016/j.ins.2022.11.118621(341-353)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.118
Chen YZhao FZhang NZhou C(2021)Semi-supervised Subspace Metric Learning2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC)10.1109/ICFTIC54370.2021.9647276(66-76)Online publication date: 12-Nov-2021
https://doi.org/10.1109/ICFTIC54370.2021.9647276
Guo XDang CLiang JWei WLiang J(2021)Metric learning with clustering-based constraintsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-021-01408-3Online publication date: 25-Aug-2021
https://doi.org/10.1007/s13042-021-01408-3
Zhou ZZhou Z(2021)Dimensionality Reduction and Metric LearningMachine Learning10.1007/978-981-15-1967-3_10(241-264)Online publication date: 21-Aug-2021
https://doi.org/10.1007/978-981-15-1967-3_10
Ye HZhan DLi NJiang Y(2020)Learning Multiple Local Metrics: Global Consideration HelpsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.290167542:7(1698-1712)Online publication date: 1-Jul-2020
https://doi.org/10.1109/TPAMI.2019.2901675
Peng YZhang NLi YYing S(2020)A Local-to-Global Metric Learning Framework From the Geometric InsightIEEE Access10.1109/ACCESS.2020.29673488(16953-16964)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2967348
Ye HZhan DJiang YZhou Z(2019)What Makes Objects Similar: A Unified Multi-Metric Learning ApproachIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.282919241:5(1257-1270)Online publication date: 1-May-2019
https://doi.org/10.1109/TPAMI.2018.2829192
Ye HZhan DJiang Y(2019)Fast generalization rates for distance metric learningMachine Language10.1007/s10994-018-5734-0108:2(267-295)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10994-018-5734-0
Chen SGong CYang JLi XWei YLi J(2018)Adversarial metric learningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3304940(2021-2027)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304889.3304940
Ye HZhan DSi XJiang Y(2017)Learning mahalanobis distance metricProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172352(3315-3321)Online publication date: 19-Aug-2017
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