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Multi-view Multi-label Learning with Incomplete Views and Labels

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Abstract

Data set with incomplete information, multi-granularity label correlation when label-specific features and complementarity information provided is ubiquitous in real-world applications. In this paper, we develop a new multi-view multi-label learning with incomplete views and labels (MVML-IVL) for solution and it is the first attempt to design a multi-view multi-label learning method with incomplete views and labels by the learning of label-specific features, label correlation matrix, low-rank assumption matrix, multi-granularity label correlation, and complementary information. Experimental results validate that (1) MVML-IVL achieves a better performance and it is superior to the classical multi-view (multi-label) learning methods in statistical; (2) the running time of MVML-IVL won’t add too much; (3) MVML-IVL has a good convergence and ability to process multi-view multi-label data sets; (4) multi-granularity label correlation plays an important role for the performance of MVML-IVL; (5) the influence of adjustable parameters is not too large.

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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.

  7. For precision, the conclusions are similar.

  8. Indeed, for other data sets, the results are similar.

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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. Furthermore, this work is also sponsored by Project funded by China Postdoctoral Science Foundation under grant number 2019M651576, National Natural Science Foundation of China (CN) under grant numbers 61602296, 61701298, Natural Science Foundation of Shanghai (CN) under grant number 16ZR1414500. The authors would like to thank their supports.

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  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Changming Zhu & Lin Ma

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  1. Changming Zhu
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Correspondence to Changming Zhu.

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Zhu, C., Ma, L. Multi-view Multi-label Learning with Incomplete Views and Labels. SN COMPUT. SCI. 3, 73 (2022). https://doi.org/10.1007/s42979-021-00957-2

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