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Label Distribution Learning with Discriminative Instance Mapping

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Advances in Knowledge Discovery and Data Mining (PAKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13935))

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Abstract

Label distribution learning (LDL) is an effective tool to tackle label ambiguity since it allows one instance to be associated with multiple labels in different degrees. Therefore, the more complex but informative label space makes it challenging to directly model the relationship between original features and label distributions. In this paper, an algorithm called Label Distribution Learning with Discriminative Instance Mapping (LDLDIM) is proposed to select a discriminative instance pool (DIP) to map the original features into a more discriminative space. First, we design a criterion that incorporates label information to quantify the discriminative power of each instance. Second, we select several instances with the highest discriminative ability to construct the DIP, and map the instances to the discriminative space through the DIP. By exploiting label information, this criterion enables the selected DIP to ensure that instances that are close (far away) in label space remain close (far away) in the discriminative space. Finally, multiple regressions for prediction are trained on the label distributions and the new features that are obtained by distance mapping with DIP. Experiments and comparisons on 16 datasets illustrate that our algorithm outperforms 6 state-of-the-art LDL methods in most cases.

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Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, ChengDu, China

    Heng-Ru Zhang, Run-Ting Bai & Wen-Tao Tang

Authors
  1. Heng-Ru Zhang
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  2. Run-Ting Bai
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  3. Wen-Tao Tang
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Corresponding author

Correspondence to Heng-Ru Zhang .

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Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Hisashi Kashima

  2. IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Tsuyoshi Ide

  3. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

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Zhang, HR., Bai, RT., Tang, WT. (2023). Label Distribution Learning with Discriminative Instance Mapping. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13935. Springer, Cham. https://doi.org/10.1007/978-3-031-33374-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-33374-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33373-6

  • Online ISBN: 978-3-031-33374-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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