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Multi-view multi-label-based online method with threefold correlations and dynamic updating multi-region

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Abstract

Semi-supervised real-time generation multi-view multi-label data sets are widely encountered in practical applications. A key issue is how to process the data whose information including labels or features may be lost due to some unforeknowable factors. In our work, we develop a multi-view multi-label-based online method with threefold correlations and dynamic updating multi-region (M2CR) to solve this issue. First, we adopt three kinds of correlations between features and labels to recover the missing information. Second, we process new arriving instances with dynamic updating multi-region. Experiments on classical multi-view multi-label data sets validate the effectiveness of M2CR in terms of classification, time performance, convergence, etc.

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Notes

  1. multi-view based multi-label propagation for image annotation

  2. multi-view conditional random fields

  3. multi-view label embedding model

  4. trigraph regularized collective matrix tri-factorization framework

  5. multi-view ensemble learning with contextual features

  6. multi-label streaming feature selection based on neighbourhood rough set

  7. semi-supervised multi-view clustering based on orthonormality-constrained NMF

  8. multi-view matrix completion

  9. multi-view vector-valued manifold regularization

  10. individuality- and commonality-based multi-view multi-label learning

  11. block-row sparse multi-view multi-label learning

  12. indeed, we get the classification performances about accuracy, acc\(^{+}\)-true positive rate, acc\(^{-}\)-true negative rate, PPV-positive predictive value, F-measure, and G-mean [64], but with the limitation of the length for paper, only performances about accuracy are shown. While this would not disturb our conclusions.

  13. the ranks are given on the base of the six classification indexes

  14. with the limitation of the length for paper, we only show the results in terms of the accuracy under the semi-supervised case. Indeed, for other cases, the conclusions are same

  15. indeed, in our experiments expect for this subsection, we show the results with a feasible percentage of training instances set, namely, the percentage accords to best parameters

  16. adjusted weight voting random forest

  17. matrix completion for multi-view weak label learning

  18. pseudo-label conditional generative adversarial imputation networks for incomplete data

  19. multi-view semi-supervised learning for classification on dynamic networks

  20. online semi-supervised active learning framework

  21. semi-supervised one-pass multi-view learning

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Acknowledgements

This work is supported by ‘Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under Grant Number 18CG54. Furthermore, this work is also sponsored by National Natural Science Foundation of China (CN) under Grant Number 61602296. The authors would like to acknowledge their supports.

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, China

    Changming Zhu, Shuaiping Guo, Dujuan Cao & YiTing Zhou

  2. Department of Computer Science & Technology, Tongji University, Shanghai, China

    Duoqian Miao

  3. Department of Electrical & Computer Engineering, University of Alberta, Alberta, Canada

    Witold Pedrycz

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  1. Changming Zhu
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Correspondence to Changming Zhu.

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Zhu, C., Guo, S., Cao, D. et al. Multi-view multi-label-based online method with threefold correlations and dynamic updating multi-region. Neural Comput & Applic 34, 6097–6117 (2022). https://doi.org/10.1007/s00521-021-06766-1

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