Abstract: To address the unreliable explicit relationships and consistency deviations among non-missing data objects in incomplete multi-view clustering, we propose a shared latent subspace-guided graph completion and clustering algorithm. Secondly, the learning of each view’s adjacency graph is guided by the subspace self-representation matrix to preserve latent consistency. Then, graph Laplacian regularization is applied to ensure that the view manifold is followed by the completed graph, by which multi-view data complementarity is maintained. Finally, consistency representation and missing data completion are integrated into a unified framework, enabling mutual enhancement between completion and clustering. Experimental results on five commonly used public datasets demonstrate the effectiveness of the proposed algorithm for incomplete multi-view clustering.

Key words: multi-view clustering, incomplete data, graph completion, latent subspace, complementarity