Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning - Ao LI; Zhuo WANG; Xiaoyang YU; Deyun CHEN; Yingtao ZHANG; Guanglu SUN (Tongxin xuebao)
Considering the impact of high dimensional data redundancy and noise interference on multiview subspace clustering, a robust multiview subspace clustering method based on multi-kernel low redundancy representation learning was proposed.Firstly, by analyzing and revealing the redundancy and noise influence characteristics of data in kernel space, a multi-kernel learning method was proposed to obtain a robust low-redundancy representation of local view-specific data, which was utilized to replace the original data to implement subspace learning.Secondly, a tensor analysis model was introduced to carry out multiview fusion, so as to learn the potential low-rank tensor structure among different subspace representations from global perspective.It would capture the high-order correlation among views while maintaining their unique information.In this method, robust low-redundancy representation learning, view-specific subspace learning and fusion potential subspace structure learning were unified into the same objective function, so that they could promote each other during iterations.A large number of experimental results demonstrate that the proposed method is superior to the existing mainstream multiview clustering methods on several objective evaluation indicators.