Title: Confident Block Diagonal Structure-Aware Invariable Graph Completion for Incomplete Multi-view Clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Incomplete multi-view clustering, nvariable graph completion, onfident block diagonal structure learningci

Abstract: Multi-view clustering (MVC) adopts complementary information from multiple views to reveal the underlying structure of the data. However, the conventional MVC-based methods remain a crucial challenge on the incomplete multi-view clustering (IMVC) tasks, when some views of the multi-view data are missing.  Particularly, current IMVC methods suffer from two main limitations: 1) they focused on recovering the missing data, yet often overlooked the potential inaccuracies in imputed values caused by the absence of true label information; 2) the recovered features were learned from the complete data, neglecting the distributional discrepancy between the complete and incomplete instances.  In order to tackle these issues, in this paper, a confident block diagonal structure-aware invariable graph completion-based incomplete multi-view clustering method (CBDS_IMVC)  is proposed. Specifically, we first design a confident-aware missing-view inferring strategy, where the confident block diagonal structures (CBDS) are learned to guarantee that recovered instances of all views have the same strict invariable local structure with the constraint of CBDS. Subsequently, we proposed an invariable graph completion strategy to learn the intrinsic structure across all views.  Each parts are jointly trained, complementing and promoting each other to achieve the optimum together. Compared to other state-of-the-art methods, the proposed CBDS_IMVC demonstrates superior performance across multiple benchmark datasets.

TL;DR: This paper proposes a tensorized confident local structure completion method for robust incomplete multi-view clustering.