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<p>Label distribution learning is a popular learning paradigm for dealing with label polysemy scenarios, providing rich semantic information and being applied in many practical tasks. However, annotating training label distributions is challenging, which inevitably introduces uncertainty, noise, and bias into the label distribution. To mitigate this issue, this paper proposes a label distribution learning method based on the estimation of label distribution robust intervals. Specifically, we first establish robust interval estimation methods for the label distribution, which captures the uncertainty in the label distribution. Subsequently, we propose a loss function and a corresponding label distribution learning algorithm, which measure the training error of the robust interval and predict the label distribution. Extensive experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the algorithm presented in this paper statistically outperforms comparison algorithms and achieves optimal performance in <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{90.63\%}\)</EquationSource> </InlineEquation> of cases.</p>

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Label distribution learning via robust interval estimation

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Peiqiu Yu,
  • Xiuyi Jia

摘要

Label distribution learning is a popular learning paradigm for dealing with label polysemy scenarios, providing rich semantic information and being applied in many practical tasks. However, annotating training label distributions is challenging, which inevitably introduces uncertainty, noise, and bias into the label distribution. To mitigate this issue, this paper proposes a label distribution learning method based on the estimation of label distribution robust intervals. Specifically, we first establish robust interval estimation methods for the label distribution, which captures the uncertainty in the label distribution. Subsequently, we propose a loss function and a corresponding label distribution learning algorithm, which measure the training error of the robust interval and predict the label distribution. Extensive experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the algorithm presented in this paper statistically outperforms comparison algorithms and achieves optimal performance in \(\varvec{90.63\%}\) of cases.