Multi-Label Learning by Exploiting Label Correlations Locally

作者：Sheng-Jun Huang, Zhi‐Hua Zhou · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2021 · DOI：10.1609/aaai.v26i1.8287 · 被引用次数：261 · 研究领域：Text and Document Classification Technologies、Machine Learning in Bioinformatics、Image Retrieval and Classification Techniques

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.