Learning latent embedding via weighted projection matrix alignment for incomplete multi-view clustering

Multi-view data are generated from multiple perspectives or diverse domains. Although many multi-view clustering methods have achieved a lot of successes based on the assumption that the data is integrity, in fact, these data may exist the case of missing instances in real applications, resulting in incomplete multi-view data. Thus, incomplete multi-view clustering has been proposed to handle this issue, which has gained considerable attention. However, for the most of existing approaches, there still have the following limitations: (i) the latent common representation information within views is not well exploited. (ii) the potential information hidden in missing views are ignored to some extent. Therefore, we proposed a novel Learning Latent Embedding via weighted projection matrix alignment for Incomplete Multi-view Clustering, termed as LLE-IMC. Specifically, a view completion model is introduced in latent embedding learning to infer the missing information. To further explore the consistency information of different views, different projection matrices are enforced to align to cluster centers by l(2,1) norm regularization. Furthermore, an efficient optimization algorithm is presented to resolve the proposed model with convergence guarantee. On several incomplete multi-view datasets, experimental results show that our proposed LLE-IMC performs better in comparison to the state-of-the-art methods, in terms of many metrics.