深度双重不完整的多视图多标签分类通过标签语义引导的对比学习
Jinrong Cui1, Yazi Xie1, Chengliang Liu2
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.
概括
本研究介绍了LSGC,这是一种用于双不完整的多视图多标签分类的新方法. 通过利用标签语义和对比学习来提高准确性,LSGC有效地处理缺失的视图和标签.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 多视图多标签学习 (MVML) 通常假定完整的数据,限制其现实世界的适用性.
- 现有的方法与包含缺失视图和不确定的标签的数据集作斗争.
- 之前的工作往往忽略或不充分利用隐藏的标签信息.
研究的目的:
- 提出一种新的方法,LSGC,用于双不完整的多视图多标签分类.
- 解决现有的MVML方法在处理缺失数据方面的局限性.
- 有效地利用标签语义和增强特征表示学习.
主要方法:
- LSGC使用深度神经网络来提取特征.
- 使用图形卷积网络来捕获标签语义和相关性.
- 一个样本标签的对比损失增强了特征学习,并补充了缺失标签的伪标签策略.
主要成果:
- 在五个标准数据集上,LSGC表现出卓越的性能.
- 该方法有效地处理双重不完整的多视图多标签分类挑战.
- 实验结果证实了利用标签语义和对比学习的有效性.
结论:
- 对于不完整的MVML问题,LSGC提供了一个强大的解决方案.
- 拟议的方法推进了多视图多标签分类的最新技术.
- 对于处理复杂的现实数据场景,LSGC提供了一个有前途的方向.
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