Confident Local Structure-Aware Incomplete Multiview Spectral Clustering
提出一种新图方法，自适应挖掘各视图可用实例间的相似结构并学习最优共识图，通过自适应权重和自信结构图缓解不完整视图信息不平衡问题，提升聚类性能。
Exploring the structure information is crucial for data clustering task, particularly for the sceneries of incomplete multiview clustering (IMVC) when some views are missing. However, almost all of the existing graph-based IMVC methods either introduce the Laplacian constraint with fixed graphs or simply fuse the graphs of all views, which are vulnerable to the quality of the constructed graphs. To address this issue, we propose a new graph-based method, called confident local structure-aware incomplete multiview spectral clustering. Different from existing works, our method seeks to adaptively uncover the inherent similarity structure among the available instances in each view and learn the optimal consensus graph within a unified learning framework. Moreover, to mitigate the adverse effects of imbalance information across incomplete views and improve the quality of consensus graph, we further impose some adaptive weights on the consensus graph learning model w.r.t. each view and introduce some confident structure graphs to explore the most confident similarity information in the model. In contrast to existing works, our approach simultaneously takes into account the pairwise similarity information and neighbor group-based confident structure information. This dual consideration makes our method more effective in achieving the optimal consensus graph and delivering superior IMVC performance. Experimental results on several datasets demonstrate that our method effectively learns a high-quality and clustering-friendly graph from incomplete multiview data, and it outperforms many state-of-the-art IMVC methods in terms of clustering performance.