Summary of Inaccurate Label Distribution Learning with Dependency Noise, by Zhiqiang Kou et al.
Inaccurate Label Distribution Learning with Dependency Noise
by Zhiqiang Kou, Jing Wang, Yuheng Jia, Xin Geng
First submitted to arxiv on: 26 May 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 This paper introduces the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to address noise in label distribution learning, caused by dependencies between instances and labels. The authors model inaccurate label distributions as a combination of true labels and noisy matrices influenced by specific instances and labels. They develop a linear mapping from instances to their true label distributions, incorporating label correlations, and decompose the noise matrix using feature and label representations with group sparsity constraints. Graph regularization is employed to align topological structures of input and output spaces, ensuring accurate reconstruction of the true label distribution matrix. The Alternating Direction Method of Multipliers (ADMM) optimizes the model efficiently. Experiments validate DN-ILDL’s ability to recover true labels accurately and establish a generalization error bound. The method outperforms existing LDL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in learning from noisy data. Imagine you’re trying to group similar things together, but some of the groups are wrong. This is called “inaccurate label distribution” and it’s hard to fix because the mistakes depend on specific instances (data points) and labels. The authors created a new way to correct these mistakes by combining true labels with noisy ones. They also used special techniques to make sure their method works well and can be applied to many different types of data. Their approach was tested and showed that it’s better than other methods at fixing the mistakes. |
Keywords
» Artificial intelligence » Generalization » Regularization
