Abstract
In multi-label classification, the explosion of the label space makes the classic multi-label classification models computationally inefficient and degrades the classification performance. To alleviate the curse of dimensionality in label space, many label space dimension reduction (LSDR) algorithms have been developed in last few years. Whereas, they are all designed for single-view learning and ignore that one sample can be represented from different views. In this paper, we propose a multi-view LSDR model for multi-label classification. The weights of different views are learned and then multi-view label embedding results are combined by the learned weights. Experiments on benchmark datasets show that the proposed multi-view learning model outperforms the best single-view model and the majority voting method.
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Acknowledgements
This work was supported by the National Program on Key Basic Research Project under Grant 2013CB329304, the National Natural Science Foundation of China under Grants 61502332, 61432011, 61222210.
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Hu, Q., Zhu, P., Zhang, C., Hu, Q. (2017). Multi-view Label Space Dimension Reduction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_27
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DOI: https://doi.org/10.1007/978-3-319-70087-8_27
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