Multiview Subspace Clustering With Grouping Effect


Multiview subspace clustering, also known as MVSC, is a relatively new method that was developed with the intention of finding the underlying subspace in multiview data and then clustering the data in accordance with the subspace that was discovered. Even though quite a few MVSC methods have been proposed over the past few years, the majority of them are unable to explicitly preserve the locality in the learned subspaces. Furthermore, they ignore the subspacewise grouping effect, which hinders their capacity for multiview subspace learning. In this article, we propose a novel method known as the MVSC with grouping effect (MvSCGE) approach to address this issue. In particular, our method simultaneously learns the multiple subspace representations for multiple views by using smooth regularization. After that, we use a unified optimization framework to take advantage of the subspacewise grouping effect that occurs in these learned subspaces. In the meantime, the approach that has been proposed is able to ensure the consistency of the cross-view and learn a consistent cluster indicator matrix for the clustering results that have been obtained. In order to validate the superiority of the proposed approach, a large number of experiments have been carried out on a variety of benchmark datasets.

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