Joint learning of latent representation and global similarity for multi-view image clustering

Lin Li; Xiaojun Zhou; Zhiqiang Lu; Dongxiao Li; Qinxu Xu; Li Song

doi:10.1117/12.2623165

16 February 2022 Joint learning of latent representation and global similarity for multi-view image clustering

Lin Li, Xiaojun Zhou, Zhiqiang Lu, Dongxiao Li, Qinxu Xu, Li Song

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120830H (2022) https://doi.org/10.1117/12.2623165
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Multi-view subspace clustering aims to divide a set of multi-source data into several groups according to their underlying subspace structure. However, the similarity matrix which is learned by most existing methods, can not well characterize data themselves more comprehensively in original data space and the global similarity the multi-view data. In this paper, we propose to jointly learn the shared latent representation by different views and the similarity matrix in a unified model. Our model can learn the shared latent representation of multi-view data from the latent space and the similarity matrix from the shared latent representation simultaneously. Experimental results on four benchmark datasets demonstrate that our method outperforms other existing competitive multi-view clustering methods.

Citation Download Citation

Lin Li, Xiaojun Zhou, Zhiqiang Lu, Dongxiao Li, Qinxu Xu, and Li Song "Joint learning of latent representation and global similarity for multi-view image clustering", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120830H (16 February 2022); https://doi.org/10.1117/12.2623165

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KEYWORDS

Data modeling

Optimization (mathematics)

Lithium

Communication engineering

Performance modeling

Visualization

Matrices

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