Summary of Data Collaboration Analysis with Orthonormal Basis Selection and Alignment, by Keiyu Nosaka et al.
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
by Keiyu Nosaka, Yuichi Takano, Akiko Yoshise
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 data collaboration (DC) approach called Orthonormal Data Collaboration (ODC) is proposed, which improves the performance of multi-source machine learning models while maintaining privacy. The traditional DC method allows arbitrary target bases, but empirical evidence shows that this choice can significantly impact model performance. To address this issue, ODC restricts the target basis to orthonormal bases, making the specific choice of basis negligible for model performance. The alignment step in ODC is reduced to the Orthogonal Procrustes Problem, which has a closed-form solution with favorable computational properties. Experimental evaluations demonstrate that ODC achieves higher accuracy and improved efficiency compared to existing DC methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data collaboration (DC) analysis allows multiple sources to train a shared machine learning model without sharing their raw data. The traditional method uses linear transformations to share data, but the choice of target basis can affect model performance. A new approach called Orthonormal Data Collaboration (ODC) is proposed, which improves model performance and efficiency while maintaining privacy. |
Keywords
* Artificial intelligence * Alignment * Machine learning
