Summary of A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends, by Xiang Li et al.
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends
by Xiang Li, Jiexi Liu, Xinrui Wang, Songcan Chen
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive survey of incomplete multi-label learning (InMLL) is presented, highlighting the challenges and potential applications of this approach to machine learning. The paper reviews the origins and limitations of InMLL, proposing a taxonomy from both data-oriented and algorithm-oriented perspectives. Real-world applications are also showcased, along with four open problems and three unexplored techniques that could inform future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Incomplete multi-label learning is a way to learn from incomplete labeled data, which can be helpful when getting complete information is difficult or impossible. This paper looks at the big picture of InMLL, including its origins, challenges, and potential uses. It also shows how InMLL has been applied in different areas and highlights some open questions that need to be answered. |
Keywords
» Artificial intelligence » Machine learning
