Self-Paced Multi-Label Learning with Diversity
This work addresses multi-label classification problems, which are incremental improvements for handling high-dimensional label spaces in machine learning applications.
The paper tackles the NP-hard challenge of multi-label learning due to large label spaces by introducing a self-paced method with diversity maintenance to avoid overfitting, resulting in effective predictive models validated on real-world datasets.
The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard. This problem can be alleviated by gradually involving easy to hard tags into the learning process. Besides, the utilization of a diversity maintenance approach avoids overfitting on a subset of easy labels. In this paper, we propose a self-paced multi-label learning with diversity (SPMLD) which aims to cover diverse labels with respect to its learning pace. In addition, the proposed framework is applied to an efficient correlation-based multi-label method. The non-convex objective function is optimized by an extension of the block coordinate descent algorithm. Empirical evaluations on real-world datasets with different dimensions of features and labels imply the effectiveness of the proposed predictive model.