[1]秦天,滕齐发,贾修一.结合局部标记序关系的弱监督标记分布学习[J].智能系统学报,2023,18(1):47-55.[doi:10.11992/tis.202204018]
QIN Tian,TENG Qifa,JIA Xiuyi.Weakly supervised label distribution learning by maintaining local label ranking[J].CAAI Transactions on Intelligent Systems,2023,18(1):47-55.[doi:10.11992/tis.202204018]
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QIN Tian,TENG Qifa,JIA Xiuyi.Weakly supervised label distribution learning by maintaining local label ranking[J].CAAI Transactions on Intelligent Systems,2023,18(1):47-55.[doi:10.11992/tis.202204018]
结合局部标记序关系的弱监督标记分布学习
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第1期
页码:
47-55
栏目:
学术论文—机器感知与模式识别
出版日期:
2023-01-05
- Title:
- Weakly supervised label distribution learning by maintaining local label ranking
- Keywords:
- label distribution learning; label polysemy; weakly supervised learning; label ranking; weakly supervised label distribution learning; multi-label learning; label correlation; local label ranking relation
- 分类号:
- TP181
- 摘要:
- 标记分布学习（label distribution learning，LDL）是一种用于解决标记多义性的新颖学习范式。现有的LDL方法大多基于完整数据信息进行设计，然而由于高昂的标注成本以及标注人员水平的局限性，很难获取到完整标注数据信息，且会导致传统LDL算法性能的下降。为此，本文提出了一种新型的结合局部序标记关系的弱监督标记分布学习算法，通过维持尚未缺失标记之间的相对关系，并利用标记相关性来恢复缺失的标记，在数据标注不完整的情况下提升算法性能。在14个数据集上进行了大量的实验来验证算法的有效性。
- Abstract:
- Label distribution learning (LDL) is a novel learning paradigm for solving labeling polysemy. Most existing LDL methods are designed based on complete data information; however, because of high labeling costs and the limitation of labelers’ level, complete labeling data information is difficult to obtain, which leads to performance degradation in traditional LDL algorithms. In this paper, we propose a novel weakly supervised LDL by maintaining a local label ranking (WSLDL-MLLR) algorithm. We improve algorithm performance under incomplete data labeling by maintaining relative relationships between the not-yet-missing labels and using label correlation to recover missing labels. Extensive experiments conducted on 14 datasets verified the effectiveness of the algorithm.
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- 相似文献/References:
-
[1]王一宾,李田力,程玉胜.结合谱聚类的标记分布学习[J].智能系统学报,2019,14(5):966.[doi:10.11992/tis.201809019]
WANG Yibin,LI Tianli,CHENG Yusheng.Label distribution learning based on spectral clustering[J].CAAI Transactions on Intelligent Systems,2019,14():966.[doi:10.11992/tis.201809019]
[2]张远健,赵天娜,苗夺谦.基于粒的标记增强标记分布学习[J].智能系统学报,2023,18(2):390.[doi:10.11992/tis.202208015]
ZHANG Yuanjian,ZHAO Tianna,MIAO Duoqian.Granule-based label enhancement in label distribution learning[J].CAAI Transactions on Intelligent Systems,2023,18():390.[doi:10.11992/tis.202208015]
备注/Memo
收稿日期:2022-04-15。
基金项目:国家自然科学基金项目(62176123);江苏省自然科学基金项目(BK20191287).
作者简介:秦天,硕士研究生,主要研究方向为机器学习和数据挖掘;滕齐发,硕士研究生,主要研究方向为机器学习和数据挖掘;贾修一,副教授,博士生导师,博士,CCF高级会员,主要研究方向为机器学习、粒计算和数据挖掘。发表学术论文60余篇
通讯作者:贾修一.E-mail:jiaxy@njust.edu.cn
更新日期/Last Update:
1900-01-01