Multi-view subspace clustering has conventionally focused on integrating heterogeneous feature descriptions to capture higher-dimensional information. One popular strategy is to generate a common subspace from different views and then apply graph-based approaches to deal with clustering. However, the performance of these methods is still subject to two limitations, namely the multiple views fusion pattern and the connection between the fusion process and clustering tasks. To address these problems, we propose a novel multi-view subspace clustering framework via fine-grained graph learning, which can tell the consistency of local structures between different views and integrate all views more delicately than previous weight regularizations. Different from other models in the literature, the point-level graph regularization and the reformulation of spectral clustering are introduced to perform graphs fusion and learn the shared cluster structure together. Extensive experiments on five real-world datasets show that the proposed framework has comparable performance to the SOTA algorithms.
翻译:多视图子空间集群通常侧重于整合不同特征描述以获取更高维度的信息。一个流行的战略是从不同观点产生一个共同的子空间,然后采用基于图形的方法处理集群问题。然而,这些方法的性能仍然受到两个限制,即多视图聚合模式以及聚合过程和组合任务之间的联系。为了解决这些问题,我们建议通过细微的图形学习,建立一个新型的多视角子空间集群框架,它能够显示不同观点之间地方结构的一致性,并比以往的重量规范化更为微妙地整合所有观点。与文献中的其他模型不同,引入了点水平图形正规化和重塑光谱集群的方法,以进行图形融合并共同学习共享的集群结构。关于五个现实世界数据集的广泛实验表明,拟议的框架与SOTA算法的性能相当。