Multi-view clustering has gained broad attention owing to its capacity exploit complementary information across multiple data views. Although existing methods demonstrate delightful performance, most of them are high time complexity and cannot handle large-scale data. Matrix factorization-based models a representative solving this problem. However, they assume that the views share dimension-fix...

Multi-view subspace clustering under a tensor framework remains challenging problem, which can be potentially applied to image classification, impainting, denoising, etc. There are some existing tensor-based multi-view models mainly making use of the consistency in different views through nuclear norm (TNN). The diversity means intrinsic difference individual view is always ignored. In this pap...

Abstract Multi-view clustering (MVC) has attracted more and attention in the recent few years by making full use of complementary consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither them jointly takes popular deep learning-based methods consideration. Therefore, this paper, we conduct a co...

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore important information hidden in missing and latent intrinsic structures each view. To tackle these issues, this paper, unified novel framework, named tensorized with graph completion (TIMVC_IGC) is proposed. Firstly, owing to effectiveness low-rank r...