Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75844
Title: Distance metric learning via iterated support vector machines
Authors: Zuo, WM
Wang, FQ
Zhang, D 
Lin, L
Huang, YC
Meng, DY
Zhang, L 
Keywords: Metric learning
Support vector machine
Kernel method
Lagrange duality
Alternating minimization
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2017, v. 26, no. 10, p. 4937-4950 How to cite?
Journal: IEEE transactions on image processing 
Abstract: 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-semi-definite 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.
URI: http://hdl.handle.net/10397/75844
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2017.2725578
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