Two-step graph propagation for incomplete multi-view clustering

Abstract

Incomplete multi-view clustering addresses scenarios where data completeness cannot be guaranteed, diverging from traditional methods that assume fully observed features. Existing approaches often overlook high-order correlations present in multiple similarity graphs, and suffer from inefficiencies due to iterative optimization procedures. To overcome these limitations, we propose a graph-based model leveraging graph propagation to effectively handle incomplete data. The proposed method translates incomplete instances into incomplete graphs, and infers missing entries through a graph propagation strategy, ensuring the inferred data is meaningful and contextually relevant. Specifically, a self-guided graph is constructed to capture global relationships, while partial graphs represent view-specific similarities. The self-guided graph is first completed through self-guided graph propagation, which subsequently aids in the propagation of the partial graphs. The key contribution of graph propagation is to propagate information from complete data to incomplete data. Furthermore, the high-order correlation across multiple views is captured by low-rank tensor learning. To enhance computational efficiency, the optimization procedure is decoupled and implemented in a stepwise manner, eliminating the need for iterative updates. Extensive experiments validate the robustness of the proposed method, demonstrating superior performance compared to state-of-the-art methods, even when all instances are incomplete.