Summary of Golden Ratio-based Sufficient Dimension Reduction, by Wenjing Yang and Yuhong Yang
Golden Ratio-Based Sufficient Dimension Reduction
by Wenjing Yang, Yuhong Yang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 proposed method uses a neural network-based approach for sufficient dimension reduction, combining the strengths of approximation capabilities for functions in Barron classes and reduced computation costs. This technique not only identifies structural dimension effectively but also estimates central space well, providing a more efficient and practical solution for high-dimensional data applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to reduce the number of variables in machine learning problems by finding important combinations that keep most of the original information. It uses neural networks to find these combinations quickly and efficiently, making it easier to work with large datasets. |
Keywords
» Artificial intelligence » Machine learning » Neural network
