Low-tubal-rank tensor factorization on constant curvature Riemann manifold with mixture of Gaussians. (September 2022)

• Record Type: Journal Article
• Authors: Ge, Qi; Shao, Wenze; Gao, Guangwei; Wang, Liqian; Wu, Fei; Wang, Tao
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Tensor factorization -- Low-tubal-rank -- Constant curvature Riemann manifold -- Mixture of Gaussians; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108180
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Tensor factorization for recovering a tensor has been applied extensively in image processing. The existing tensor factorization methods have shown effective performance, however, they ignore that the plenty of low-rank components of the tensors in the real scenarios are manifold-valued. To capture more precise low-rank components, we propose a tensor factorization model with a low-rank subspace representation on the constant curvature Riemann manifold. In addition, to make the tensor factorization method more capable of adaptive complex noise, we model the noise on each mode of a tensor with a specific mixture of Gaussians (MoG). The experiments on images and videos demonstrate that the proposed method outperforms the state-of-the-art methods.