结构规范化共识动态图学习不完整的多视图集群
1School of Computer and Software, Hohai University, Nanjing, China; School of Information and Computer, Anhui Polytechnic University, Wuhu, China.
概括
本研究介绍了一种用于不完整多视图聚类 (IMVC) 的新算法,该算法包含结构信息和特征权重. 新方法通过相互促进共识图学习和缺失特征恢复来提高集群准确性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 不完整的多视图集群 (IMVC) 算法对于数据分析至关重要,但往往忽略了特征重要性和结构信息.
- 现有的基于动态图的IMVC方法在利用原始特征空间结构和个体特征重量方面存在局限性.
研究的目的:
- 提出一个新的IMVC算法 (SRCDAGL-IMC),解决现有方法的局限性.
- 从所有视角利用结构信息,并纳入样本特定的特征权重.
- 为了同时恢复缺失的功能,并学习共识图.
主要方法:
- 开发了SRCDAGL-IMC算法,将结构信息作为规范化术语.
- 引入了样本系数,以权衡每个视图中的单个特征的重要性.
- 在模型培训中采用有效的交替优化策略.
- 同时执行共识图学习和缺失特征恢复.
主要成果:
- 拟议的SRCDAGL-IMC算法与基于最先进的矩阵分解的IMVC方法相比,表现优越.
- 在六个公共数据集的准确性,规范化的相互信息和纯度方面观察到显著的改进.
- 结构信息和特征权重的整合在提高集群质量方面被证明是有效的.
结论:
- SRCDAGL-IMC有效地解决了现有的IMVC算法的关键局限性.
- 拟议的方法通过整合结构信息和特征权重,为不完整的多视图集群提供了一个强大的框架.
- 这些发现表明了未来研究多视图学习和数据归算的有希望的方向.
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