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A simple multiple-fold correlation-based multi-view multi-label learning

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Abstract

Correlations among different features and labels are ubiquitous in the present multi-view multi-label data sets and they are always described with within-view, cross-view, and consensus-view representations. While how to discover and measure these correlations effectively so as to enhance performances of a learning machine is an open problem, this problem cannot be solved by existing traditional learning machines. In this article, different from the current classical multi-view, multi-label, multi-view multi-label learning machines, we focus on the simultaneously measurement of multiple-fold correlations including within-view ones, cross-view ones, and consensus-view ones. Then, a simple multiple-fold correlation-based multi-view multi-label learning (MC-MVML) is developed. Extensive experiments on 36 classical data sets validate the superiority of MC-MVML in terms of classification performance, training time, convergence, statistical analysis, influence of parameters, etc., and the development of multi-view multi-label learning theory is expected to be promoted.

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Data availibility

The data sets analyzed during the current study are available in the different repositories or from different organizations and the details of them can be found in Table 4.

Notes

  1. \(||Y_j-X_jW_j||_F^2\) is reconstructed as \(||Y_{ij}U_{ij-j}-X_{ij}V_{ij-j}W_{ij-j}^YU_{ij-j}||_F^2\), \(||Y_cU_{cj}-X_cV_{cj}W_{cj}^YU_{cj}||_F^2\), \(||Y_{ij}U_{ij-j}-X_{ij}V_{ij-j}V_{ij-j}^TW_{ij-j}^X||_F^2\), \(||Y_cU_{cj}-X_cV_{cj}V_{cj}^TW_{cj}^X||_F^2\).

  2. Iteration time means the time when the changes of normalized objective value is smaller than 0.01.

  3. We adopt AUC for the elaboration due to the conclusions addressed from precision are similar.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (CN) under grant numbers 62276164 and 61602296, ‘Science and technology innovation action plan’ Natural Science Foundation of Shanghai under grant number 22ZR1427000. Furthermore, this work is also sponsored by ‘Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under grant number 18CG54. The authors would like to thank their supports.

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, No. 1550, Lingang Avenue, Pudong, 201306, Shanghai, China

    Changming Zhu, Jiyun Zhao, Yilin Dong, Lei Cao, Fan Zhou, Yuhu Shi, Lai Wei & Rigui Zhou

  2. School of Computer and Artificial Intelligence, Zhengzhou University, No.100, Science Avenue, Zhengzhou, 10587, Henan, China

    Shizhe Hu

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  1. Changming Zhu
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Correspondence to Changming Zhu.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, ‘A simple multiple-fold correlation-based multi-view multi-label learning.’

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Zhu, C., Zhao, J., Hu, S. et al. A simple multiple-fold correlation-based multi-view multi-label learning. Neural Comput & Applic 35, 10407–10420 (2023). https://doi.org/10.1007/s00521-023-08241-5

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