Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 121, Issue -, Pages 59-84Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2020.02.003
Keywords
Label distribution learning; Multi-label learning; Label ambiguity; Sparse dictionary learning
Categories
Funding
- National Natural Science Foundation of China [61672276, 61572242]
- Natural Science Foundation of Jiangsu Province of China [BK20161406]
Ask authors/readers for more resources
Label distribution learning (LDL) is a generalized machine learning framework for dealing with label ambiguity, as it can explore the relative importance levels of different labels in the description of each sample. Although several algorithms have been proposed to solve LDL problems, they usually destroy the consistency of geometric structures between feature space and label space to a certain extent, which frequently plays a significant role in learning tasks. Meanwhile, most existing LDL algorithms only take predictive performances into consideration, while ignoring the computational cost and robustness to noises. To remedy above deficiencies, we propose a novel algorithm, i.e., Local Collaborative Representation based Label Distribution Learning, shortly LCR-LDL. In LCR-LDL, an unlabeled sample is treated as the collaborative representation of the local dictionary constructed by the neighborhood of the unlabeled sample, and the discriminatory information of representation coefficients is used to reconstruct the label distribution of the unlabeled sample. Experimental results on 20 real-world LDL data sets compared with results produced by 11 state-of-the-art algorithms show that, the proposed LCR-LDL algorithm can not only effectively improve the predictive performances for LDL tasks, but also exhibit higher robustness and a lightweight computational overhead. This study suggests new trends for considering the computational cost and robustness issues in the LDL community. (C) 2020 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available