Impact Factor:11.683

Journal:IEEE Transactions on Neural Networks and Learning Systems

Key Words:Low-rank representation, multiview clustering, t-linear, t-product, tensor space

Abstract:The ubiquitous information from multiple-view data, as well as the complementary information among different views, is usually beneficial for various tasks, for example, clustering, classification, denoising, and so on. Multiview subspace clustering is based on the fact that multiview data are generated from a latent subspace. To recover the underlying subspace structure, a successful approach adopted recently has been sparse and/or low-rank subspace clustering. Despite the fact that existing subspace clustering approaches may numerically handle multiview data, by exploring all possible pairwise correlation within views, high-order statistics that can only be captured by simultaneously utilizing all views are often overlooked. As a consequence, the clustering performance of the multiview data is compromised. To address this issue, in this paper, a novel multiview clustering method is proposed by using t-product in the third-order tensor space. First, we propose a novel tensor construction method to organize multiview tensorial data, to which the tensor-tensor product can be applied. Second, based on the circular convolution operation, multiview data can be effectively represented by a t-linear combination with sparse and low-rank penalty using "self-expressiveness." Our extensive experimental results on face, object, digital image, and text data demonstrate that the proposed method outperforms the state-of-the-art methods for a range of criteria.

Co-author:Junbin Gao,Shengli Xie,Yi Guo

First Author:Ming Yin

Indexed by:Journal paper

Issue:2019, 30(3)

Page Number:851-864

Translation or Not:no

Date of Publication:2019-03-01

Included Journals:SCI