Affiliation:
1. School of Science and Information Science, Qingdao Agricultural University, Qingdao 266109, China
2. Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China
Abstract
Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the ℓ2,1 norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single view, compromising the clustering performance. Here, we design a robust tensor learning method for multi-view spectral clustering (RTL-MSC), which employs the weighted tensor nuclear norm to regularize the essential tensor for exploiting the high-order correlations underlying multiple views and adopts the nuclear norm to constrain each frontal slice of the essential tensor as the block diagonal matrix. Simultaneously, a novel column-wise sparse norm, namely, ℓ2,p, is defined in RTL-MSC to measure the error tensor, making it sparser than the one derived by the ℓ2,1 norm. We design an effective optimization algorithm to solve the proposed model. Experiments on three widely used datasets demonstrate the superiority of our method.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Natural Science Foundation of Shandong Province
Natural Science Foundation of Guangdong Province
Qingdao Agricultural University
Qingchuang Talents Induction Program of Shandong Higher Education Institution
Reference41 articles.
1. Normalized cuts and image segmentation;Shi;IEEE Trans. Pattern Anal. Mach. Intell.,2000
2. On spectral clustering: Analysis and an algorithm;Ng;Adv. Neural Inf. Process. Syst.,2001
3. A simple approach to automated spectral clustering;Fan;Adv. Neural Inf. Process. Syst.,2022
4. Efficient semidefinite spectral clustering via Lagrange duality;Yan;IEEE Trans. Image Process.,2014
5. Learning segmentation by random walks;Meila;Adv. Neural Inf. Process. Syst.,2000