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231225s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2725578
|2 doi
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|a eng
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| 100 |
1 |
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|a Wangmeng Zuo
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Distance Metric Learning via Iterated Support Vector Machines
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|c 2017
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|a Text
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|a ƒaComputermedien
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|a Date Completed 11.12.2018
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|a Date Revised 11.12.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely positive-semidefinite constrained metric learning (PCML) and nonnegative-coefficient constrained metric learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification, and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training
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|a Journal Article
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| 700 |
1 |
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|a Faqiang Wang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, David
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liang Lin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yuchi Huang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Deyu Meng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lei Zhang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 26(2017), 10 vom: 15. Okt., Seite 4937-4950
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|g year:2017
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|g day:15
|g month:10
|g pages:4937-4950
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|u http://dx.doi.org/10.1109/TIP.2017.2725578
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