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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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GrooveSquid.com Paper Summaries

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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