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.

多视图群集通过探索多个视图的补充信息将一组数据划分为多个组。主流试图将多视图数据投影到一个共同共享的子空间中，并进一步发现真正的数据结构。理想情况下，子空间中的聚类应该彼此共享较少的语义，以便可以获取不同的组，而在以前的著作中不能保证这种排他性。为此，本文提出了一种基于非负矩阵分解的子空间学习方法，用于排他性多视图聚类，其中对聚类的排他性施加了双正交约束。此外，为提高聚类性能，所提出的方法还利用了可用的标记数据并将其扩展为半监督方式。尤其，通过合并传播的半监督流形正则化，可以在我们的方法中丰富和编码有限的监督信息，以指导学习过程。可以通过导出的迭代更新规则来解决制定的优化问题。在七个公共数据集上的实验结果证明了它与其他最新方法相比的有希望的性能。