Multiview clustering via exclusive non-negative subspace learning and constraint propagation

Multiview clustering partitions a set of data into groups by exploring complementary information of multiple views. The mainstream tries to project the multiview data into a commonly shared subspace and further discover the true data structure. Ideally, clusters in the subspace should share less semantics with each other so that distinct groups can be obtained while this exclusivity is not guaranteed in previous works. To this end, this paper proposes a non-negative matrix factorization based subspace learning method for exclusive multiview clustering, where the double-orthogonal constraints are imposed for the cluster exclusivity. Moreover, to boost the clustering performance, the proposed method also exploits the available labeled data and is extended into a semi-supervised manner. Particularly, by incorporating the propagated semi-supervised manifold regularizations, the limited supervised information is enriched and encoded in our method to guide the learning process. The formulated optimization problem can be solved by the derived iterative updating rules. Experimental results on seven public datasets demonstrate its promising performance against other state-of-the-art approaches. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This study proposes a non-negative matrix factorization method for exclusive multiview clustering, with double-orthogonal constraints imposed for cluster exclusivity. Additionally, the method utilizes labeled data and is extended into a semi-supervised manner to enhance clustering performance. Incorporating propagated semi-supervised manifold regularizations, the method enriches limited supervised information to guide the learning process.