Summary of Robust Capped Lp-norm Support Vector Ordinal Regression, by Haorui Xiang et al.
Robust Capped lp-Norm Support Vector Ordinal Regression
by Haorui Xiang, Zhichang Wu, Guoxu Li, Rong Wang, Feiping Nie, Xuelong Li
First submitted to arxiv on: 25 Apr 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 The paper proposes a novel approach to Ordinal Regression, specifically addressing the issue of outliers in real-world data. It presents a Capped _{p}-Norm loss function that is robust to both light and heavy outliers, which can misguide the learning process. This is achieved through the use of a weight matrix to detect and eliminate outliers during training. The proposed model, CSVOR, outperforms state-of-the-art methods in the presence of outliers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with a type of machine learning called Ordinal Regression. It’s used when we have data that has an order or ranking. But sometimes, this data can be bad or incorrect, which makes it hard for computers to learn from it. The new model, CSVOR, is designed to ignore the bad data and focus on the good data, making it better at learning from real-world data. |
Keywords
» Artificial intelligence » Loss function » Machine learning » Regression
