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OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification

文献类型：期刊论文

作者Deng, Huiyuan1; Meng, Xiangzhu2; Wang, Huibing3; Feng, Lin1
刊名INFORMATION SCIENCES
出版日期2023-11-01
卷号648页码:18
ISSN号0020-0255
关键词Multi-metric learning Alternating direction method Nearest-neighbor classification Riemannian manifold
DOI10.1016/j.ins.2023.119550
通讯作者Feng, Lin(fenglin@dlut.edu.cn)
英文摘要Multi-metric learning-a method to learn multiple local metrics to reveal the feature's correlations of samples from different local regions-has become an essential tool to measure the similarities between instances from heterogeneous datasets. However, most existing cluster-based MML methods first partition the training data with a predefined metric and then learn multiple metrics via the local instances, leading to these two independent procedures fail to cooperate with each other. In this paper, we propose an Optimal instance Partition-based Multi-Metric Learning (OPM2L) method for heterogeneous dataset classification by unifying the instance partition and multiple local metrics learning into a single objective. In particular, multiple anchor centers together with a global metric are employed to assist the instance partition process. During the training, the shared information contained in local metrics is aggregated into the global metric by a dedicated regularizer, which improves the instance partition process and offers the subsequent multiple local metrics learning with more informative instances. Moreover, an efficient alternating direction technology is employed to seek a feasible solution to the proposed method. We further confirmed that the sub-problems can be settled with closed-form solutions, while the superiority of the proposed method is also proved by experimental results on extensive datasets.
资助项目National Natural Science Foundation of PR China[61972064] ; Liaoning Revitalization Talents Program[XLYC1806006] ; Fundamental Research Funds for the Central Universities[DUT19RC (3) 012]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:001070036100001
资助机构National Natural Science Foundation of PR China ; Liaoning Revitalization Talents Program ; Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/53110]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Feng, Lin
作者单位1.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian, Peoples R China
2.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing, Peoples R China
3.Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian 116024, Peoples R China
推荐引用方式
GB/T 7714		 Deng, Huiyuan,Meng, Xiangzhu,Wang, Huibing,et al. OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification[J]. INFORMATION SCIENCES,2023,648:18.
APA Deng, Huiyuan,Meng, Xiangzhu,Wang, Huibing,&Feng, Lin.(2023).OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification.INFORMATION SCIENCES,648,18.
MLA Deng, Huiyuan,et al."OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification".INFORMATION SCIENCES 648(2023):18.

入库方式： OAI收割

来源：自动化研究所

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