Metric learning with relative distance constraints: A modified SVM approach

Changchun Luo; Mu Li; Hongzhi Zhang; Faqiang Wang; David Zhang; Wangmeng Zuo

Metric learning with relative distance constraints: A modified SVM approach

Changchun Luo^*
, Mu Li
, Hongzhi Zhang
, Faqiang Wang
, David Zhang
, Wangmeng Zuo

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Hong Kong Polytechnic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.

Original language	English
Title of host publication	Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings
Editors	Hongzhi Wang, Wanxiang Che, Zhaowen Qiu, Zhongyuan Han, Junyu Lin, Haoliang Qi, Zeguang Lin, Leilei Kong
Publisher	Springer New York LLC
Pages	242-249
Number of pages	8
ISBN (Electronic)	9783662462478
State	Published - 2015
Externally published	Yes
Event	International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015 - Harbin, China Duration: 10 Jan 2015 → 12 Jan 2015

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	503
ISSN (Print)	1868-4238

Conference

Conference	International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015
Country/Territory	China
City	Harbin
Period	10/01/15 → 12/01/15

Keywords

Kernel method
Lagrange duality
Mahalanobis distance
Metric learning
Support vector machine

Cite this

Luo, C., Li, M., Zhang, H., Wang, F., Zhang, D., & Zuo, W. (2015). Metric learning with relative distance constraints: A modified SVM approach. In H. Wang, W. Che, Z. Qiu, Z. Han, J. Lin, H. Qi, Z. Lin, & L. Kong (Eds.), Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings (pp. 242-249). (IFIP Advances in Information and Communication Technology; Vol. 503). Springer New York LLC.

Luo, Changchun ; Li, Mu ; Zhang, Hongzhi et al. / Metric learning with relative distance constraints : A modified SVM approach. Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings. editor / Hongzhi Wang ; Wanxiang Che ; Zhaowen Qiu ; Zhongyuan Han ; Junyu Lin ; Haoliang Qi ; Zeguang Lin ; Leilei Kong. Springer New York LLC, 2015. pp. 242-249 (IFIP Advances in Information and Communication Technology).

@inproceedings{4a8a68253cee4005b597173e2efea08d,

title = "Metric learning with relative distance constraints: A modified SVM approach",

abstract = "Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.",

keywords = "Kernel method, Lagrange duality, Mahalanobis distance, Metric learning, Support vector machine",

author = "Changchun Luo and Mu Li and Hongzhi Zhang and Faqiang Wang and David Zhang and Wangmeng Zuo",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015 ; Conference date: 10-01-2015 Through 12-01-2015",

year = "2015",

language = "英语",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer New York LLC",

pages = "242--249",

editor = "Hongzhi Wang and Wanxiang Che and Zhaowen Qiu and Zhongyuan Han and Junyu Lin and Haoliang Qi and Zeguang Lin and Leilei Kong",

booktitle = "Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings",

}

Luo, C, Li, M , Zhang, H, Wang, F, Zhang, D & Zuo, W 2015, Metric learning with relative distance constraints: A modified SVM approach. in H Wang, W Che, Z Qiu, Z Han, J Lin, H Qi, Z Lin & L Kong (eds), Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings. IFIP Advances in Information and Communication Technology, vol. 503, Springer New York LLC, pp. 242-249, International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Harbin, China, 10/01/15.

Metric learning with relative distance constraints: A modified SVM approach. / Luo, Changchun; Li, Mu ; Zhang, Hongzhi et al.
Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings. ed. / Hongzhi Wang; Wanxiang Che; Zhaowen Qiu; Zhongyuan Han; Junyu Lin; Haoliang Qi; Zeguang Lin; Leilei Kong. Springer New York LLC, 2015. p. 242-249 (IFIP Advances in Information and Communication Technology; Vol. 503).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Metric learning with relative distance constraints

T2 - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

AU - Luo, Changchun

AU - Li, Mu

AU - Zhang, Hongzhi

AU - Wang, Faqiang

AU - Zhang, David

AU - Zuo, Wangmeng

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2015.

PY - 2015

Y1 - 2015

N2 - Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.

AB - Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.

KW - Kernel method

KW - Lagrange duality

KW - Mahalanobis distance

KW - Metric learning

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/84927749973

M3 - 会议稿件

AN - SCOPUS:84927749973

T3 - IFIP Advances in Information and Communication Technology

SP - 242

EP - 249

BT - Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings

A2 - Wang, Hongzhi

A2 - Che, Wanxiang

A2 - Qiu, Zhaowen

A2 - Han, Zhongyuan

A2 - Lin, Junyu

A2 - Qi, Haoliang

A2 - Lin, Zeguang

A2 - Kong, Leilei

PB - Springer New York LLC

Y2 - 10 January 2015 through 12 January 2015

ER -

Luo C, Li M , Zhang H, Wang F, Zhang D, Zuo W. Metric learning with relative distance constraints: A modified SVM approach. In Wang H, Che W, Qiu Z, Han Z, Lin J, Qi H, Lin Z, Kong L, editors, Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings. Springer New York LLC. 2015. p. 242-249. (IFIP Advances in Information and Communication Technology).

Metric learning with relative distance constraints: A modified SVM approach

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Keywords

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