Summary of High-dimensional Learning with Noisy Labels, by Aymane El Firdoussi et al.
High-dimensional Learning with Noisy Labels
by Aymane El Firdoussi, Mohamed El Amine Seddik
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 novel theoretical study explores high-dimensional binary classification with class-conditional noisy labels. A linear classifier is proposed, featuring a label noisiness aware loss function, to investigate its performance in large-scale datasets. By relying on random matrix theory and assuming a Gaussian mixture data model, the paper shows that the optimal classifier in low dimensions does not generalize well to high-dimensional settings. Instead, an optimized method is designed to efficiently handle noisy labels in high dimensions, which outperforms existing baselines in experiments using real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers examine how to accurately classify data when some of the labels are incorrect. They create a new type of linear classifier that takes into account the possibility of noisy labels and test it on large datasets. The results show that methods that work well in low-dimensional settings often don’t work as well when dealing with high-dimensional data. To solve this problem, the researchers develop an optimized approach that can handle noisy labels more effectively. |
Keywords
» Artificial intelligence » Classification » Loss function
