南京大学学报(自然科学版) ›› 2023, Vol. 59 ›› Issue (3): 388397.doi: 10.13232/j.cnki.jnju.2023.03.003
Yifan Zhang1,2, Ting Li1,2, Hongwei Ge1,2()
摘要:
目前,多视图子空间聚类在模式识别和机器学习领域得到了广泛的研究.以前的多视图聚类算法大多将多视图数据划分在其原始特征空间中,其功效在很大程度上隐式地依赖于原始特征呈现的质量.此外,不同视图包含同一对象的特定信息,如何利用这些视图恢复潜在的多样性信息对后续聚类尤其重要.为了解决上述问题,提出一种多样性诱导的潜在嵌入多视图聚类方法,使用特定于视图的投影矩阵从多视图数据中恢复潜在嵌入空间.考虑到多视图数据不同视图之间的多样性信息,采用经验的希尔伯特施密特独立准则约束特定于视图的投影矩阵,将潜在嵌入学习、多样性学习、全局相似性学习和聚类指标学习整合在同一框架中,还设计了一种交替优化方案来有效处理优化问题.在几个真实的多视图数据集上的实验表明,提出的算法具有一定的优势.
中图分类号:
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