Learning With Auxiliary Less-Noisy Labels
文献类型：期刊论文
| 作者 | Duan, Yunyan1,2; Wu, Ou3
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2017-07-01 |
| 卷号 | 28期号:7页码:1716-1721 |
| 关键词 | Maximum Likelihood Approach Noisy Degrees Noisy Labels Soft Constraints |
| DOI | 10.1109/TNNLS.2016.2546956 |
| 文献子类 | Article |
| 英文摘要 | 111111; Obtaining a sufficient number of accurate labels to form a training set for learning a classifier can be difficult due to the limited access to reliable label resources. Instead, in real-world applications, less-accurate labels, such as labels from nonexpert labelers, are often used. However, learning with less-accurate labels can lead to serious performance deterioration because of the high noise rate. Although several learning methods (e.g., noise-tolerant classifiers) have been advanced to increase classification performance in the presence of label noise, only a few of them take the noise rate into account and utilize both noisy but easily accessible labels and less-noisy labels, a small amount of which can be obtained with an acceptable added time cost and expense. In this brief, we propose a learning method, in which not only noisy labels but also auxiliary less-noisy labels, which are available in a small portion of the training data, are taken into account. Based on a flipping probability noise model and a logistic regression classifier, this method estimates the noise rate parameters, infers ground-truth labels, and learns the classifier simultaneously in a maximum likelihood manner. The proposed method yields three learning algorithms, which correspond to three prior knowledge states regarding the less-noisy labels. The experiments show that the proposed method is tolerant to label noise, and outperforms classifiers that do not explicitly consider the auxiliary less-noisy labels. |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000404048300020 |
| 资助机构 | NSFC(61379098) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12022]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 通讯作者 | Wu, Ou |
| 作者单位 | 1.Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China 2.Northwestern Univ, Dept Linguist, Evanston, IL 60201 USA 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Yunyan,Wu, Ou. Learning With Auxiliary Less-Noisy Labels[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(7):1716-1721. |
| APA | Duan, Yunyan,&Wu, Ou.(2017).Learning With Auxiliary Less-Noisy Labels.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(7),1716-1721. |
| MLA | Duan, Yunyan,et al."Learning With Auxiliary Less-Noisy Labels".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.7(2017):1716-1721. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。