Abstract: Aiming at the problems that how to fuse the rich information of multi-views into the consensus graph and avoid the sub-optimal performance caused by the spectral embedding in the subsequent processing, we proposed a multi-view clustering algorithm based on diversity consensus spectral embedding learning. In order to learn the consensus graph better, the algorithm automatically learnt the consensus spectral embedding matrix and the discrete clustering label matrix under the consideration of the diversity of views. By comparing with other algorithms on real data sets, the superiority of the proposed algorithm in improving clustering performance was proved.

Key words:  , consensus graph, automatic weighting, spectral embedding, multi-view clustering