题名 | Learning from Crowds via Joint Probabilistic Matrix Factorization and Clustering in Latent Space |
作者 | |
发表日期 | 2021 |
会议名称 | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) |
会议录名称 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
![]() |
ISSN | 0302-9743 |
卷号 | 12460 LNAI |
页码 | 546-561 |
会议日期 | SEP 14-18, 2020 |
会议地点 | ELECTR NETWORK |
摘要 | Learning from noisy labels is getting trendy in the era of big data. However, in crowdsourcing practice, it is still a challenging task to extract ground truth labels from noisy labels obtained from crowds. In this paper, we propose a latent variable model built on probabilistic logistic matrix factorization model and classical Gaussian mixture model for inferring ground truth labels from noisy, crowdsourced ones. The proposed model incorporates item heterogeneity in contrast to previous works and allows for vector space embeddings of both items and worker labels. Moreover, we derive a tractable mean-field variational inference algorithm to approximate the model posterior. Meanwhile, related MAP approximation problem to the model posterior is also investigated to identify links to existing works. Empirically, we demonstrate that the proposed method achieves good inference accuracy while preserving meaningful uncertainty measures in the embeddings, and therefore better reflects the intrinsic structure of data. |
关键词 | Crowdsourcing Label aggregation Latent variable models Variational inference |
DOI | 10.1007/978-3-030-67667-4_33 |
URL | 查看来源 |
收录类别 | CPCI-S |
语种 | 英语English |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications |
WOS记录号 | WOS:000718580000033 |
Scopus入藏号 | 2-s2.0-85103273956 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/9763 |
专题 | 个人在本单位外知识产出 |
通讯作者 | Yao, Wuguannan |
作者单位 | 1.Department of Mathematics,City University of Hong Kong,Kowloon,Hong Kong 2.School of Data Science,City University of Hong Kong,Kowloon,Hong Kong |
推荐引用方式 GB/T 7714 | Yao, Wuguannan,Lee, Wonjung,Wang, Junhui. Learning from Crowds via Joint Probabilistic Matrix Factorization and Clustering in Latent Space[C], 2021: 546-561. |
条目包含的文件 | 条目无相关文件。 |
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。
修改评论