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Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise

文献类型：期刊论文

作者Zeng, Fanxia1,3; Zhang, Wensheng2,3; Zhang, Siheng2,3; Zheng, Nan1
刊名NEUROCOMPUTING
出版日期2019-02-22
卷号330页码:138-150
关键词Resampling scheme KISSME Distance metric learning Label noise
ISSN号0925-2312
DOI10.1016/j.neucom.2018.11.009
通讯作者Zhang, Wensheng(wensheng.zhang@ia.ac.cn)
英文摘要Distance metric learning aims to learn a metric with the similarity of samples. However, the increasing scalability and complexity of dataset or complex application brings about inevitable label noise, which frustrates the distance metric learning. In this paper, we propose a resampling scheme robust to label noise, Re-KISSME, based on Keep It Simple and Straightforward Metric (KISSME) learning method. Specifically, we consider the data structure and the priors of labels as two resampling factors to correct the observed distribution. By introducing the true similarity as latent variable, these two factors are integrated into a maximum likelihood estimation model. As a result, Re-KISSME can reason the underlying similarity of each pair and reduce the influence of label noise to estimate the metric matrix. Our model is solved by iterative algorithm with low computational cost. With synthetic label noise, the experiments on UCI datasets and two application datasets of person re-identification confirm the effectiveness of our proposal. (C) 2018 Elsevier B.V. All rights reserved.
WOS关键词PERSON REIDENTIFICATION ; CLASSIFICATION
资助项目National Key R&D Program of China[2017YFC0803700] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61602482] ; National Natural Science Foundation of China[61501463] ; Beijing Natural Science Foundation[4172063]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000454789500014
出版者ELSEVIER SCIENCE BV
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/25631]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhang, Wensheng
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Fanxia,Zhang, Wensheng,Zhang, Siheng,et al. Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise[J]. NEUROCOMPUTING,2019,330:138-150.
APA Zeng, Fanxia,Zhang, Wensheng,Zhang, Siheng,&Zheng, Nan.(2019).Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise.NEUROCOMPUTING,330,138-150.
MLA Zeng, Fanxia,et al."Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise".NEUROCOMPUTING 330(2019):138-150.

入库方式： OAI收割

来源：自动化研究所

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