期刊
APPLIED INTELLIGENCE
卷 52, 期 3, 页码 3175-3192出版社
SPRINGER
DOI: 10.1007/s10489-021-02601-1
关键词
Multi-label classification; Dictionary learning; Analysis discriminative dictionary; Incoherence promotion; Label relationships
资金
- Natural Science Foundation of China [62076074, 61876044, 61672169]
- Guangdong Basic and Appiled Basic Research Foundation [2020A151010670, 2020A151011501]
- Science and Technology Planning Project of Guangzhou [202002030141]
The paper introduces a new method called ADML for multi-label learning, which combines analytical discrimination dictionary learning and sparse representation to achieve success in multi-label classification.
Multi-label learning is a machine learning classification problem, in which an example belongs to more than one classes at the same time. Recently, multi-label learning has aroused a great deal of attention, and has achieved great success in the fields of text and image classification. In this paper, we propose a new method for multi-label learning, which is named as analysis discriminative dictionary learning for multi-label classification (ADML). We first incorporate analytical discrimination dictionary learning and sparse representation into multi-label classifier to obtain a unified model. The incoherence promoting term and reconstruction error for each label are minimized to obtain the dictionary. We then incorporate an analysis inconsistency promotion term into the model, which minimizes the reconstruction error of the dictionary with the corresponding label of the data. Further, we calculate a linear classifier by taking the label relationships into account. It is worth noting that we implicitly consider the label relationships in the analysis dictionary and linear classifier. Finally, we conduct experiments on 15 datasets to test the performance of the proposed ADML method and baselines. The results show that the proposed ADML method can deliver higher performance than previous multi-label methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据