Tri-Structured-Sparsity Induced Joint Feature Selection and Classification for Hybrid Noise Resilient Multilabel Learning
Multilabel learning handles the problem that instances are associated with multiple labels. In practical applications, multilabel learning often suffers from imperfect training data. Typically, labels may be noisy or features may be corrupted, or both. Most existing multilabel learning models only c...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9113479/ |