Online semi-supervised learning with learning vector quantization

doi:10.1016/j.neucom.2020.03.025

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Online semi-supervised learning with learning vector quantization
	Shen, Yuan-Yuan1,2 ; Zhang, Yan-Ming1,2 ; Zhang, Xu-Yao1 ; Liu, Cheng-Lin1,2,3
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-07-25
卷号	399 页码:467-478
通讯作者	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
摘要	Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning, in which the data appear in a sequential manner with a mixture of both labeled and unlabeled samples. Despite the recent advances, there are still many unsolved problems in this area. In this paper, we propose a novel OSSL method based on learning vector quantization (LVQ). LVQ classifiers, which represent the data of each class by a set of prototypes, have found their usage in a wide range of pattern recognition problems and can be naturally adapted to the online scenario by updating the prototypes with stochastic gradient optimization. However, most of the existing LVQ algorithms were designed for supervised classification. To extract useful information from unlabeled data, we propose two simple and computationally efficient methods based on clustering assumption. To be specific, we use the maximum conditional likelihood criterion for updating prototypes when data sample is labeled, and the Gaussian mixture clustering criterion or neural gas clustering criterion for adjusting prototypes when data sample is unlabeled. These two criteria are utilized alternatively according to the availability of label information to make full use of both supervised and unsupervised data to boost the performance. By extensive experiments, we show that the proposed method exhibits higher accuracy compared with the baseline methods and graph-based methods and is much more efficient than graph-based methods in both training and test time. (c) 2020 Elsevier B.V. All rights reserved.
关键词	Online learning Semi-supervised classification Learning vector quantization Gaussian mixture distribution Neural gas
DOI	10.1016/j.neucom.2020.03.025
关键词[WOS]	NEURAL-GAS ; CLASSIFICATION ; ALGORITHM
收录类别	SCI
语种	英语
资助项目	Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61836014] ; National Natural Science Foundation of China (NSFC)[61773376] ; National Natural Science Foundation of China (NSFC)[61721004]
项目资助者	Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000536504100012
出版者	ELSEVIER
七大方向——子方向分类	模式识别基础
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39523
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Liu, Cheng-Lin
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Shen, Yuan-Yuan,Zhang, Yan-Ming,Zhang, Xu-Yao,et al. Online semi-supervised learning with learning vector quantization[J]. NEUROCOMPUTING,2020,399:467-478.
APA	Shen, Yuan-Yuan,Zhang, Yan-Ming,Zhang, Xu-Yao,&Liu, Cheng-Lin.(2020).Online semi-supervised learning with learning vector quantization.NEUROCOMPUTING,399,467-478.
MLA	Shen, Yuan-Yuan,et al."Online semi-supervised learning with learning vector quantization".NEUROCOMPUTING 399(2020):467-478.