多视图图卷积子空间聚类
Multi-View Graph Convolutional Subspace Clustering
DOI: 10.12677/pm.2024.149328, PDF,    国家自然科学基金支持
作者: 王经纬, 唐科威：辽宁师范大学数学学院，辽宁 大连
关键词: 子空间聚类；多视图聚类；图卷积；F范数；加权机制；Subspace Clustering； Multi-View Clustering； Graph Convolution； F-Norm； Weighting Mechanism
摘要: 子空间聚类在最近几年受到了广泛的关注，新近提出的自适应图卷积子空间聚类方法取得了很好的效果。但是该方法仅适用于单一视图的子空间聚类问题。本文将该方法拓展到多视图上，提出了多视图图卷积子空间聚类。该方法构建了F范数正则项以便更有效地挖掘每个视图中数据之间的关系，还构建了不同视图之间的加权机制来融合不同视图之间的信息。大量的实验证明，我们的方法是有效的。
Abstract: Subspace clustering has received extensive attention in recent years. Although the recently proposed adaptive graph convolutional subspace clustering performs well, it can only be applied to subspace clustering problem with a single view. This paper proposes multi-view graph convolutional sub-space clustering to extend this method to the multi-view situation. This method not only constructs F-norm regularization to more effectively mine the relationships between data in each view, but also builds a weighting strategy between different views to integrate their information. A large number of experiments have proved that our method is effective.
文章引用：王经纬, 唐科威. 多视图图卷积子空间聚类[J]. 理论数学, 2024, 14(9): 67-77. https://doi.org/10.12677/pm.2024.149328

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