Proceedings

Learn the Highest Label and Rest Label Description Degrees

Jing Wang, Xin Geng

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3097-3103.

https://doi.org/10.24963/ijcai.2021/426

Keywords

  • Machine Learning: Multi-instance; Multi-label; Multi-view learning
  • Machine Learning: Structured Prediction

Abstract

Although Label Distribution Learning (LDL) has found wide applications in varieties of classification problems, it may face the challenge of objective mismatch -- LDL neglects the optimal label for the sake of learning the whole label distribution, which leads to performance deterioration. To improve classification performance and solve the objective mismatch, we propose a new LDL algorithm called LDL-HR. LDL-HR provides a new perspective of label distribution, \textit{i.e.}, a combination of the \textbf{highest label} and the \textbf{rest label description degrees}.

It works as follows. First, we learn the highest label by fitting the degenerated label distribution and large margin. Second, we learn the rest label description degrees to exploit generalization. Theoretical analysis shows the generalization of LDL-HR. Besides, the experimental results on 18 real-world datasets validate the statistical superiority of our method.

Jing Wang, and Xin Geng. 2021. Learn the Highest Label and Rest Label Description Degrees. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3097–3103. https://doi.org/10.24963/ijcai.2021/426
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