Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers
Hyunha NAM, Hirotaka Hachiya, Masashi Sugiyama · IEICE Transactions on Information and Systems · 2013
Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.