Abstract: Aiming at how to utilize the implicit information in multi-view data and avoid the problem of sub-optimal clustering performance 
in the subsequent processing, we proposed a multi-view clustering algorithm with low\|rank tensor based on consistency and difference. Firstly, the algorithm simultaneously considered the consistency and differential information of views, and superimposed multiple consistent similarity matrices in a tensor  constrainted by low-rank  to explore the higher-order correlations of the information between views, thus obtaining higher-quality similarity matrices. Secondly, clustering results were directly obtained by learning a consistent non-negative embedding matrix. Thirdly, an adaptive weighting strategy was used to consider the importance of different view data. Finally, the effectiveness of the algorithm on the multi-view clustering problem was verified by comparison experiments with other algorithms on six real datasets.

Key words: multi-view clustering, consistency, difference, low-rank tensor representation, adaptive weighting