Summary of New Solutions Based on the Generalized Eigenvalue Problem For the Data Collaboration Analysis, by Yuta Kawakami et al.
New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis
by Yuta Kawakami, Yuichi Takano, Akira Imakura
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 method for confidential data analysis, called Data Collaboration Analysis (DCA), which enables sharing of data between multiple institutions while protecting sensitive information. The approach optimizes collaborative functions to improve analytical accuracy and reduce computational costs. A key contribution is the formulation of an optimization problem using matrix segmentation and generalized eigenvalue problems. The proposed solution outperforms existing methods in terms of predictive accuracy, as demonstrated through experiments on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a big problem with sharing data between different organizations while keeping sensitive information private. Right now, it’s hard to share data because of the computational cost and communication load. The authors propose a new way to do this called Data Collaboration Analysis (DCA). They also develop a method to optimize how the data is shared, making it more accurate and efficient. This approach has been tested on real-world data and shows better results than current methods. |
Keywords
» Artificial intelligence » Optimization
