Abstract:
Multiview subspace clustering (MVSC) is a new method that finds the subspace in multiview data and clusters it. Many MVSC methods have been proposed recently, but most of them cannot explicitly preserve locality in the learned subspaces and neglect the subspacewise grouping effect, which limits their multiview subspace learning ability. This article proposes a novel MVSC with grouping effect (MvSCGE) approach. Our approach simultaneously learns multiple subspace representations for multiple views with smooth regularization and exploits the subspacewise grouping effect in these learned subspaces using a unified optimization framework. The proposed method ensures cross-view consistency and learns a consistent cluster indicator matrix for final clustering results. Extensive experiments on several benchmark datasets have proven the proposed approach’s superiority.
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