Label distribution learning with correlation information

doi:10.1016/j.engappai.2025.111591

Label distribution learning with correlation information

2025-07-30

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DOI：10.1016/j.engappai.2025.111591

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Label distribution learning quantifies the label space for each instance and has broad applicability in various fields. However, most existing works primarily focus on label correlation, but they have a deficiency in capturing instance correlation. Meanwhile, traditional label distribution learning operates on individual labels sequentially, which restricts the potential application of common features. Therefore, in this paper, we propose a novel approach for label distribution learning with correlation information, i.e., instance correlation and label correlation. Specifically, the instance correlation and label correlation are identified by an optimization function and Pearson correlation coefficient, respectively. Besides, we conduct ℓ2,1 regularization to exploit common features. Comprehensive experiments conducted on twelve publicly datasets demonstrate the effectiveness of our proposed approach against other well-established label distribution learning algorithms.

Keywords:

label distribution learning

instance correlation

label correlation

common features

ℓ2,1 regularization

Label distribution learning with correlation information

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