两十年的多视图集群:分类学,应用和挑战
IEEE transactions on pattern analysis and machine intelligence
|December 4, 2025
概括
本调查提供了多视图集群 (MVC) 方法的全面概述,并根据技术,融合策略和场景分类它们. 它还强调了机器学习领域的未来研究方向.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 多视图集群 (MVC) 利用来自多个数据源的互补信息.
- 在过去的二十年中,MVC取得了重大进展.
- 缺乏对现有的MVC方法和未来挑战的全面总结.
研究的目的:
- 提供对现有的多视图集群方法的彻底审查.
- 根据技术,融合策略和场景对MVC方法进行分类.
- 确定和讨论MVC的未来研究方向和挑战.
主要方法:
- 将MVC方法分为七种技术,四种融合策略和五种场景.
- 收集和分析常用的MVC数据集.
- 在基准数据集上对典型的MVC方法的性能分析.
主要成果:
- 对当前的MVC方法的结构化概述.
- 确定六个不同的应用领域,包括计算机视觉,信息检索和生物信息学.
- 为该领域提出了七个有前途的未来研究方向.
结论:
- 该调查提供了多视图集群的系统审查和分类.
- 它提供了对当前应用程序和性能基准的见解.
- 确定的未来方向旨在指导多视图聚类的后续研究.
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