Multi-Label Learning by Exploiting Label Correlations Locally

Authors

  • Sheng-Jun Huang Nanjing University
  • Zhi-Hua Zhou Nanjing University

DOI:

https://doi.org/10.1609/aaai.v26i1.8287

Abstract

It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances. In real-world tasks, however, different instances may share different label correlations, and few correlations are globally applicable. In this paper, we propose the ML-LOC approach which allows label correlations to be exploited locally. To encode the local influence of label correlations, we derive a LOC code to enhance the feature representation of each instance. The global discrimination fitting and local correlation sensitivity are incorporated into a unified framework, and an alternating solution is developed for the optimization. Experimental results on a number of image, text and gene data sets validate the effectiveness of our approach.

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Published

2021-09-20

How to Cite

Huang, S.-J., & Zhou, Z.-H. (2021). Multi-Label Learning by Exploiting Label Correlations Locally. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 949-955. https://doi.org/10.1609/aaai.v26i1.8287

Issue

Section

AAAI Technical Track: Machine Learning