Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2019 | ACML | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Amjad Seyedi | 1 | 0 | 0.34 |
| S. Siamak Ghodsi | 2 | 0 | 0.34 |
| Fardin Akhlaghian | 3 | 0 | 0.34 |
| Mahdi Jalili | 4 | 314 | 37.98 |
| Parham Moradi | 5 | 430 | 18.41 |