Incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning

Xuemei Han, Zhenwen Ren, Chuanyun Zou, Xiaojian You

Abstract: Highlights•A novel method for incomplete multi-view subspace clustering is proposed.•The missing samples recovering and structural information learning are integrated.•The consistent and specific-view structural information are learned simultaneously.•Schatten p<math><mi is="true">p</mi></math>-norm is applied to capture the global manifold consistent information.•Experimental results demonstrate the effectiveness of our method.