Recent advances on supervised distance metric learning algorithms

被引：0
作者：
机构：
[1] School of Information Science and Technology, Xiamen University, Xiamen
来源：
Yan, Yan (yanyan@xmu.edu.cn) | 1600年 / Science Press卷 / 40期
基金：
中国国家自然科学基金;
关键词：
Distance metric learning; Mahalanobis distance; Non-pairwise constraints; Pairwise constraints;
D O I：
10.3724/SP.J.1004.2014.02673
中图分类号：
学科分类号：
摘要：
Recently, distance metric learning has become one of the most attractive research areas in computer vision and pattern recognition. How to learn an effective distance metric to measure the similarity between subjects is a key problem. A large number of algorithms have been proposed to deal with supervised distance metric learning. This paper reviews and discusses recently developed algorithms for supervised distance metric learning. Based on the partition of pairwise constraints and non-pairwise constraints, some representative algorithms are introduced and their respective pros and cons are analyzed. The prospects for future development and suggestions for further research work are presented in the end.
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收藏
页码：2673 / 2686
页数：13
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