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Multiview clustering via consistent and specific nonnegative matrix factorization with graph regularization

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Abstract

Multiview clustering is a hot research topic in machine learning and computer vision, and many non-negative matrix factorization (NMF)-based multiview clustering approaches have been proposed. However, most existing NMF-based multiview clustering methods aim to only push the learned latent feature matrices of all views towards a common consensus representation or only consider the consistency among all multiview data, whereas the complementary information between different views is often ignored. In this work, we propose a novel multi-view clustering via consistent and specific nonnegative matrix factorization with graph regularization method (MCCS), where the consistency among all multiview data and view-specific information in each view data are simultaneously considered. Specifically, the NMF problem is first formulated using a shared consistent basis matrix, consistent coefficient matrix, a set of view-specific basis matrices, and view-specific coefficient matrices. Then, manifold regularization is embedded into the objective function to preserve the intrinsic geometrical structure of the original data space. Furthermore, a disagreement term is designed to push these view-specific coefficient matrices further towards a common consensus and to ensure that the multiple views have the same underlying cluster structure. Moreover, the multiplicative update algorithm is employed to optimize the objective function. Extensive experimental results on five multiview benchmark datasets, namely, BBC, BBCSport, 20NGs, Wikipedia, and Handwritten, demonstrate that the proposed MCCS outperforms state-of-the-art methods, achieving improvements of 2.29%, 6.63%, 16.15%, 6.51%, and 2.85%, respectively, over the MVCC method in terms of NMI.

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Notes

  1. http://mlg.ucd.ie/datasets/segment.html.

  2. http://lig-membres.imag.fr/grimal/data.html.

  3. http://www.svcl.ucsd.edu/projects/crossmodal/.

  4. http://archive.ics.uci.edu/ml/datasets/Multiple+Features.

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Authors and Affiliations

  1. Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, People’s Republic of China

    Haixia Xu, Limin Gong, Haizhen Xuan, Xusheng Zheng & Xianbing Wen

  2. Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, People’s Republic of China

    Zan Gao

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  1. Haixia Xu
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  2. Limin Gong
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  3. Haizhen Xuan
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  4. Xusheng Zheng
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  5. Zan Gao
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  6. Xianbing Wen
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Correspondence to Haizhen Xuan or Zan Gao.

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Communicated by B.-K. Bao.

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Manuscript received Oct 19th, 2021; This work was supported in part by the National Natural Science Foundation of China (No. 61872270, No. 61572357, No. 61971309). Young creative team in universities of Shandong Province (No. 2020KJN012), Jinan 20 projects in universities (No. 2020GXRC040, No. 2018GXRC014). New Artificial Intelligence project towards the integration of education and industry in Qilu University of Technology (No. 2020KJC-JC01).

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Xu, H., Gong, L., Xuan, H. et al. Multiview clustering via consistent and specific nonnegative matrix factorization with graph regularization. Multimedia Systems 28, 1559–1572 (2022). https://doi.org/10.1007/s00530-022-00905-x

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