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Weak-label-based global and local multi-view multi-label learning with three-way clustering

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Abstract

This paper develops a weak-label-based global and local multi-view multi-label learning with three-way clustering (WL-GLMVML-ATC) to solve multi-view multi-label data sets and exploit more authentic global and local label correlations of both the whole data set and each view simultaneously. Different from the traditional learning methods, WL-GLMVML-ATC pays more attention to the solutions of weak-label cases and uncertain relationships of clusters with the usage of Universum and active three-way clustering. According to Universum notion, even though the size of labeled instances is much more smaller than the unlabeled ones, the useful sample information can still be enhanced. Through the active three-way clustering strategy, the belongingness of instances to a cluster depend on the probabilities of uncertain instances belonging to core regions. This strategy brings a more authentic local label correlation since many traditional methods suppose that instances and the corresponding clusters always exhibit certain relationships such as belong-to definitely and not belong-to definitely. This hypothesis is not ubiquitous in real-world applications. According to the experiments, we can see WL-GLMVML-ATC (1) achieves a better performance, be superior to the classical multi-view learning methods and multi-label learning methods in statistical, advances the development of these learning methods in final; (2) won’t add too much running time; (3) has a good convergence and ability to process multi-view multi-label data sets.

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Notes

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

  2. http://archive.ics.uci.edu/ml/datasets/Reuters+RCV1+RCV2+Multilingual%2C+Multiview+Text+Categorization+Test+collection

  3. http://archive.ics.uci.edu/ml/datasets/Corel+Image+Features

  4. http://host.robots.ox.ac.uk/pascal/VOC/

  5. http://press.liacs.nl/mirflickr/

  6. http://mlg.ucd.ie/datasets/3sources.html

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Acknowledgements

This work is sponsored by ‘Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under grant number 18CG54. This work is also supported by Project funded by China Postdoctoral Science Foundation under grant number 2019M651576, National Natural Science Foundation of China (CN) under grant number 61602296, Natural Science Foundation of Shanghai under grant number 16ZR1414500. The authors would like to thank their supports.

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Correspondence to Changming Zhu.

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Zhu, C., Cao, D., Guo, S. et al. Weak-label-based global and local multi-view multi-label learning with three-way clustering. Int. J. Mach. Learn. & Cyber. 13, 1337–1354 (2022). https://doi.org/10.1007/s13042-021-01450-1

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