- Session 6: Supervised Learning -- Day 3 (Nov.19), poster session: 11:30-14:00, talks: 14:10-15:25 (5th floor Hall 2)
- Poster number: Tue30
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Ze-Sen Chen (Southeast University); Min-Ling Zhang (Southeast University)
Multi-label learning learns from examples each associated with multiple class labels simultaneously, and the goal is to induce a predictive model which can assign a set of relevant labels for the unseen instance. Label-specific features serve as an effective strategy towards inducing multi-label predictive model, where the relevancy of each class label is determined by employing tailored features encoding inherent and distinct characteristics of the class label its own. In this paper, a regularization based approach named REEL is proposed for label-specific features generation, which works by enriching label-specific feature representation for each class label via synergizing informative label-specific features from other class labels with sparse regularization. Specifically, full-order label correlations are considered by REEL while the number of classifiers induced for multi-label prediction is linear to the number of class labels. Extensive experiments on fifteen benchmark multi-label data sets clearly show the favorable performance of REEL against other state-of-the-art multi-label learning approaches with label-specific features.