Online semi-supervised learning with learning vector quantization
文献类型：期刊论文
| 作者 | Shen, Yuan-Yuan1,2 ; Zhang, Yan-Ming1,2; Zhang, Xu-Yao1; Liu, Cheng-Lin1,2,3
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 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. |
| WOS关键词 | NEURAL-GAS ; CLASSIFICATION ; ALGORITHM |
| 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研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000536504100012 |
| 出版者 | ELSEVIER |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC) |
| 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.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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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