Fast Incomplete Multi-view Clustering with Adaptive Similarity Completion and Reconstruction

Recently, anchor-based incomplete multi-view clustering (IMVC) has been widely adopted for fast clustering, but most existing approaches still encounter some issues: (1) They generally rely on the observed samples to construct anchor graphs, ignoring the potentially useful information of missing instances. (2) Most methods attempt to learn a consensus anchor graph, failing to fully excavate the complementary information and high-order correlations across views. (3) They generally apply post-processing on learned anchor graph to seek latent embeddings, making them not globally-optimal. To address these issues, this paper proposes a novel fast IMVC approach with Adaptive Similarity Completion and Reconstruction (ASCR), which unifies anchor learning, anchor-sample similarity construction and completion, and latent multi-view embedding learning in a joint framework. Specifically, ASCR learns an anchor-sample similarity graph for each view, and the missing values are fulfilled to mitigate the adverse effects. To explore the consistent and complementary information across views, ASCR simultaneously seeks the view-specific anchor embeddings and sample embeddings in a latent subspace by similarity reconstruction, which not only preserves the semantic information into latent embeddings but also enhances the low-rank property of similarity graphs, achieving a reliable graph completion process. Furthermore, the high-order cross-view correlations are explored with tensor-based regularization. Extensive experimental results demonstrate the superiority and efficiency of ASCR compared with SOTA approaches.