Summary of Towards Better Performance in Incomplete Ldl: Addressing Data Imbalance, by Zhiqiang Kou et al.
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
by Zhiqiang Kou, Haoyuan Xuan, Jing Wang, Yuheng Jia, Xin Geng
First submitted to arxiv on: 17 Oct 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 Label Distribution Learning (LDL) is a machine learning paradigm that tackles label ambiguity issues. Existing Incomplete Label Distribution Learning (InLDL) methods overlook label distribution imbalance, leading to the proposal of InComplete and Imbalance Label Distribution Learning (I2LDL), a framework addressing both incomplete and imbalanced labels. I2LDL decomposes the label distribution matrix into low-rank frequent labels and sparse rare labels, capturing head and tail label structures. The model is optimized using Alternating Direction Method of Multipliers (ADMM) and backed by generalization error bounds via Rademacher complexity. Experimental results on 15 real-world datasets demonstrate I2LDL’s effectiveness and robustness compared to existing InLDL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to learn from labels that are not always clear. When we have incomplete or imbalanced label distributions, it can be hard to learn effectively. The authors propose a new framework called I2LDL that handles both incomplete and imbalanced labels. They do this by breaking down the label distribution into two parts: common and rare labels. This helps them capture patterns in the data better. The authors tested their approach on 15 real-world datasets and showed it works well compared to existing methods. |
Keywords
» Artificial intelligence » Generalization » Machine learning
