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Hierarchical metric learning with intra-level and inter-level regularization

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Abstract

Metric learning for hierarchical classification is a significant problem whose purpose is to learn more discriminative metrics by exploiting the dataset’s hierarchical structure and achieving higher accuracy rates for hierarchical classification. However, most of the existing hierarchical metric learning methods fail to consider the irrelevance between the metrics of sibling nodes in a hierarchical tree, which makes the metric of each node not well distinguish child nodes. This paper proposes a hierarchical metric learning model based on intra-level and inter-level regularization. The model mines the idiosyncrasies of sibling nodes and learns a more discriminative metric for each non-leaf node. At the same time, by exploiting the commonalities of parent-child nodes to control inter-level error propagation. Extensive experiments on five hierarchical datasets demonstrate that the proposed algorithm can perform better than the existing ones.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (No.2019YFE0118200), the National Natural Science Foundation of China (Nos.61976184, 61772323), and the 1331 Engineering Project of Shanxi Province, China.

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Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Lin Li, Ting Li, Wei Wei, Xinyao Guo & Jiye Liang

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  1. Lin Li
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  2. Ting Li
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  3. Wei Wei
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  4. Xinyao Guo
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  5. Jiye Liang
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Correspondence to Wei Wei.

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Li, L., Li, T., Wei, W. et al. Hierarchical metric learning with intra-level and inter-level regularization. Int. J. Mach. Learn. & Cyber. 13, 4033–4042 (2022). https://doi.org/10.1007/s13042-022-01664-x

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  • DOI: https://doi.org/10.1007/s13042-022-01664-x

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