Abstract
Partial multi-label learning aims to induce a multi-label classifier from partial multi-label data in which each instance is annotated with a number of candidate labels, but only a subset of them are valid. Although much progress has been made in this field, most of the existing methods fail to make the most of the instance correlations in partial multi-label data. In this paper, we propose a novel partial multi-label learning method that exploits instance correlations to eliminate noisy labels and induces a multi-label classifier by learning a linear mapping from the feature space to the label space. Experiments on the real-world partial multi-label dataset verify the effectiveness of the proposed method.
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Acknowledgments
The corresponding author of this work is Jiachen Sun. This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 23KJB520009, and in part by the Philosophy and Social Foundation of the Jiangsu Higher Education Institutions of China under Grants 2022SJYB0466 and 2022SJYB0471, and in part by the Provincial General Project of Innovation and Entrepreneurship Training Program for College Students under Grant 202310329063Y, and in part by the High-Level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute under Grant JSPI21GKZL401.
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Gao, G., Zhan, Z., Sun, J., Sun, A., Lan, H. (2024). Partial Multi-label Learning with Instance Correlations. In: Zhang, Y., Qi, L., Liu, Q., Yin, G., Liu, X. (eds) Proceedings of the 13th International Conference on Computer Engineering and Networks. CENet 2023. Lecture Notes in Electrical Engineering, vol 1126. Springer, Singapore. https://doi.org/10.1007/978-981-99-9243-0_42
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DOI: https://doi.org/10.1007/978-981-99-9243-0_42
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