通过自适应图形学习和光谱旋转进行单步多视图集群
IEEE transactions on neural networks and learning systems
|April 10, 2024
概括
本研究介绍了一种新的单步多视图集群方法,即自适应图形学习和光谱旋转 (AGLSR),它将图形学习和集群统一起来,以提高性能. AGLSR通过自适应式学习图表和从光谱嵌入中直接生成离散标签来提高集群精度.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机科学 计算机科学
背景情况:
- 基于图形的多视图集群方法通常涉及多个步骤,将图形学习与分区生成分开.
- 这种分离可能会导致低于最佳的性能,因为它无法密切整合这些过程.
研究的目的:
- 提出一种新的一步多视图集群方法,将图形学习和分区生成统一起来.
- 通过克服传统多步骤方法的局限性来提高集群性能.
主要方法:
- 引入了自适应图形学习和光谱旋转 (AGLSR) 方法.
- 它适应地学习每个视图的亲和度图,以捕捉样本关系.
- 一种光谱嵌入技术利用共享的特征空间,光谱旋转直接产生离散的集群标签.
主要成果:
- 拟议的AGLSR方法在基准数据集的六个指标中显示出有效性.
- 为了优化,开发了一种有效的更新算法,具有经过验证的收性.
- 一步式方法比传统方法更紧密地整合了图形学习和集群.
结论:
- AGLSR为多视图集群提供了更有效和统一的方法.
- 该方法通过光谱旋转直接生成离散标签来实现卓越的性能.
- 该研究提供了一个强大的算法,证明了实验验证.
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