Label Distribution Learning
By Xin Geng, Rongzi Ji. 2013 IEEE 13th International Conference on Data Mining Workshops, 2013
609 citations recorded by the source corpus.
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named label distribution learning (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare their performance, six evaluation measures are suggested for LDL algorithms, and the first batch of real-world label distribution datasets are prepared. Experimental results on the ten real-world datasets show clear advantage of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a4b4baa5d861347c21b435d938d84e5ac0ded9f2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
- Ivor Wai-Hung Tsang
- Haobo Wang
- Weiwei Liu
- Xiaobo Shen
- Xin Geng
- Min-Ling Zhang
- Trevor Darrell
- Kate Saenko
Citations in the corpus
- Binary relevance for multi-label learning: an overview (2018, background): “Different forms of auxiliary information exist, including ordinal scale over each class label [40], full ranking over relevant class labels [41], importance distribution over all class labels [43,44], and oracle feedbacks over queried labels of unlabeled examples [45].”
- Deep Label Distribution Learning With Label Ambiguity (2016, neutral): “…0 −18 −36 −54 −72 −90 0 0.02 0.04 0.06 0.08 YawPitch P ro b a b ili ty (-3◦,-27◦) −90 −72 −54 −36 −18 0 18 36 54 72 90 90 72 54 36 18 0 −18 −36 −54 −72 −90 0 0.01 0.02 0.03 0.04 0.05 YawPitch P ro b a b ili ty (+27◦,+6◦) −90 −72 −54 −36 −18 0 18 36 54 72 90 90 72 54 36 18 0 −18 −36 −54 −72 −90…”
- The Emerging Trends of Multi-Label Learning (2020, methodology): “Intelligent systems are enrolled in increasingly difficult and complicated tasks, and thus new settings like PML and LDL deserve more attention.”