Multi-Label Learning with Global and Local Label Correlation

By Yue Zhu, James T. Kwok, Zhi-Hua Zhou. IEEE Transactions on Knowledge and Data Engineering, 2017

364 citations recorded by the source corpus.

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3eb05768d824747c9998e5d86a5df39be9c2d1de

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

1. Xiaobo Shen
2. Ivor Wai-Hung Tsang
3. Haobo Wang
4. Weiwei Liu
5. Mingsheng Long
6. Kate Saenko
7. Dong Xu
8. Wen Li

In Information Systems

1. Xiaobo Shen
2. Ivor Wai-Hung Tsang
3. Haobo Wang
4. Weiwei Liu

In Signal Processing

1. Ivor Wai-Hung Tsang
2. Weiwei Liu
3. Mingsheng Long
4. Jianmin Wang
5. Qiang Yang
6. Sinno Jialin Pan
7. Boqing Gong

Citations in the corpus

• The Emerging Trends of Multi-Label Learning (2020, methodology): “smoothness [79] such that the candidate label set can be fully disambiguated.”
• AGCN: Augmented Graph Convolutional Network for Lifelong Multi-Label Image Recognition (2022, neutral): “In traditional multi-label learning, label relationships are verified effective to improve the recognition [21, 22].”