Summary of Decentralized Kernel Ridge Regression Based on Data-dependent Random Feature, by Ruikai Yang et al.
Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature
by Ruikai Yang, Fan He, Mingzhen He, Jie Yang, Xiaolin Huang
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML)
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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 Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). The paper proposes a new decentralized KRR algorithm that pursues consensus on decision functions, allowing greater flexibility and adaptation to varying data on different nodes. This approach achieves an average regression accuracy improvement of 25.5% across six real-world datasets, while maintaining the same communication costs as other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to do decentralized kernel ridge regression (KRR). Right now, people use random features (RF) for consistency in KRR. But sometimes, data on different nodes is very different, so we need ways to generate different RFs that adapt to the data. The algorithm works by getting consensus on decision functions, which makes it more flexible and better at adapting to different data. |
Keywords
» Artificial intelligence » Regression
