Summary of Random Features Outperform Linear Models: Effect Of Strong Input-label Correlation in Spiked Covariance Data, by Samet Demir et al.
Random Features Outperform Linear Models: Effect of Strong Input-Label Correlation in Spiked Covariance Data
by Samet Demir, Zafer Dogan
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Random Feature Model (RFM) is a powerful tool for understanding training and generalization performance in high-dimensional learning. While it’s been shown that RFM performs similarly to noisy linear models under certain conditions, empirical evidence suggests that RFM often outperforms linear models in real-world applications. To investigate this disparity, we analyze the RFM under anisotropic input data with spiked covariance, where dimensions diverge while maintaining finite ratios. Our findings reveal that strong correlations between inputs and labels are a key factor enabling RFM to surpass linear models. Additionally, we show that RFM performs similarly to noisy polynomial models, with the degree of polynomiality dependent on the strength of input-label correlation. These insights are validated through numerical simulations, confirming RFM’s superiority in scenarios featuring strong input-label correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Random Feature Model (RFM) is a new way to study learning in high-dimensional spaces. Right now, we don’t fully understand why this model sometimes works better than others. This paper tries to fix that by looking at the RFM when the data has special structures. We found out that if the input and output are strongly connected, the RFM can do a much better job than simpler models. It’s like having a superpower! The paper also shows that this model is similar to other powerful models we have seen before, but with some important differences. Overall, this research helps us understand when the RFM will be super helpful and when it might not be so great. |
Keywords
» Artificial intelligence » Generalization
