一项关于用于多视图数据的表示学习的调查
Yalan Qin1, Xinpeng Zhang1, Shui Yu2
1School of Communication and Information Engineering, Shanghai University, China.
概括
这项调查为多视图集群算法提供了一个新的分类,分为自我监督和非自我监督的类别. 它提供了现有方法的全面概述,帮助研究人员在这个快速增长的机器学习领域.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 多视图集群利用来自不同数据源的信息.
- 现有的调查往往忽视了自我监督和非自我监督方法的整合.
- 这一领域在过去几十年中出现了显著的增长.
研究的目的:
- 提供多视图集群算法的新调查和分类.
- 将现有方法分为自我监督和非自我监督的框架.
- 提供对多视图集群的发展有洞察力的概述.
主要方法:
- 分类为非自主监督和自主监督的多视图集群.
- 对非自我监督方法的审查:非表示学习 (矩阵分解,内核) 和表示学习 (图形,深度,子空间).
- 自主监督方法的审查:对比和生成方法.
主要成果:
- 多视图聚类技术的结构化分类.
- 详细检查每个类别内的各种算法.
- 在监督和无监督学习模式中确定关键方法.
结论:
- 该调查将现有知识整合到多视图集群中.
- 它强调了考虑自我监督和非自我监督方法的重要性.
- 为机器学习和数据挖掘领域的研究人员提供了宝贵的资源.
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