# Self-Paced Multi-Label Learning with Diversity

arXiv: https://arxiv.org/abs/1910.03497 · HTML: https://mlsanity.com/paper/1910.03497/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective](https://mlsanity.com/paper/2510.23507/) (2025)

## Abstract (arXiv)

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.