对于不完整的多视图集群,关系的完成
Minghong Wu1, Jihua Zhu1, Wenbiao Yan2
1School of Software Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Yunnan Key Laboratory of Intelligent Systems and Computing, Kunming, 650500, China.
本研究引入了一种新的方法,通过完成关系信息以有效处理缺失的数据来进行不完整的多视图集群. 该方法确保了数据完整性,并减少了模糊性,以提高聚类性能.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机视觉 计算机视觉
背景情况:
- 现实世界的多视图数据往往会缺少或不完整的样本.
- 现有的不完整多视图集群 (IMVC) 方法在利用结构信息,维护数据完整性和处理集群不确定性方面存在困难.
研究的目的:
- 提出一种新的IMVC方法,解决现有方法的局限性.
- 通过利用结构关系和确保数据完整性,有效处理缺失的值.
主要方法:
- 一个新的关系完成模块旨在直接完成缺失视图的关系.
- 注意层融合和高信心引导利用多视图补充信息.
- 语义对比学习和多视图标签分布学习增强了一致的信息提取.
主要成果:
- 提出的方法有效地解决了缺失的值,同时保持数据完整性和减轻错误传播.
- 在多个真实数据集上的实验结果表明,拟议的方法优于最先进的技术.
结论:
- 新型关系完成模块显著提高了IMVC的性能.
- 该方法有效处理不完整的多视图数据,为集群挑战提供了强大的解决方案.
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