Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/208260
Title: Distance Metric Learning via Iterated Support Vector Machines
Authors: Wangmeng Zuo;Faqiang Wang;David Zhang;Liang Lin;Yuchi Huang;Deyu Meng;Lei Zhang
Year: 2017
Publisher: IEEE
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-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.
URI: http://localhost/handle/Hannan/208260
volume: 26
issue: 10
More Information: 4937,
4950
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7973168.pdf3.3 MBAdobe PDF
Title: Distance Metric Learning via Iterated Support Vector Machines
Authors: Wangmeng Zuo;Faqiang Wang;David Zhang;Liang Lin;Yuchi Huang;Deyu Meng;Lei Zhang
Year: 2017
Publisher: IEEE
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-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.
URI: http://localhost/handle/Hannan/208260
volume: 26
issue: 10
More Information: 4937,
4950
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7973168.pdf3.3 MBAdobe PDF
Title: Distance Metric Learning via Iterated Support Vector Machines
Authors: Wangmeng Zuo;Faqiang Wang;David Zhang;Liang Lin;Yuchi Huang;Deyu Meng;Lei Zhang
Year: 2017
Publisher: IEEE
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-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.
URI: http://localhost/handle/Hannan/208260
volume: 26
issue: 10
More Information: 4937,
4950
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7973168.pdf3.3 MBAdobe PDF