Online semi-supervised learning with learning vector quantization

doi:10.1016/j.neucom.2020.03.025

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 模式分析与学习团队

	Online semi-supervised learning with learning vector quantization
	Shen, Yuan-Yuan 2,3; Zhang, Yan-Ming 2,3; Zhang, Xu-Yao 2; Liu, Cheng-Lin 1,2,3
刊名	NEUROCOMPUTING
	2020-07-25
卷号	399 页码:467-478
关键词	Online learning Semi-supervised classification Learning vector quantization Gaussian mixture distribution Neural gas
ISSN号	0925-2312
DOI	10.1016/j.neucom.2020.03.025
通讯作者	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
英文摘要	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.
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]
WOS关键词	NEURAL-GAS ; CLASSIFICATION ; ALGORITHM
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000536504100012
资助机构	Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/39522]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Liu, Cheng-Lin
作者单位	1.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
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.