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Feature-label dual-mapping for missing label-specific features learning

  • Data analytics and machine learning
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Abstract

Label-specific features learning can effectively exploit the unique features of each label, which alleviates the high dimensionality and improves the classification performance of multi-label. However, most existing label-specific features learning algorithms assume that label space is complete, ignoring the effect of missing labels on the classification accuracy. Some methods try to recover the missing labels first and then learn the mapping between the completed label matrix and the feature matrix. However, early intervention in the recovery of missing labels may affect the distribution of original labels to a certain extent. In this paper, feature-label dual-mapping for missing label-specific features learning is proposed. According to the information that the label depends on the feature, the dual-mapping weight of the complete feature space and the missing label space is jointly learned. Therefore, the proposed algorithm is to conduct latent missing labels recovery by feature-label dual-mapping to directly obtain target weight in this paper, avoiding the negative influence of early label recovery intervention. Compared with several state-of-the-art methods in 10 benchmark multi-label data sets, the results show that the proposed algorithm is reasonable and effective.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61702012 and Key Laboratory of Data Science and Intelligence Application, Fujian Province University (NO. D202005) and Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education (Anhui University) (No.2020A003).

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Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246133, China

    Lulu Zhang, Yusheng Cheng, Yibin Wang & Gensheng Pei

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, 363000, Fujian, China

    Yusheng Cheng & Yibin Wang

Authors
  1. Lulu Zhang
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  2. Yusheng Cheng
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  3. Yibin Wang
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  4. Gensheng Pei
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Correspondence to Yusheng Cheng.

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The authors declared that they have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest is connected with the work submitted.

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Zhang, L., Cheng, Y., Wang, Y. et al. Feature-label dual-mapping for missing label-specific features learning. Soft Comput 25, 9307–9323 (2021). https://doi.org/10.1007/s00500-021-05884-1

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