Summary of Interpretation Of High-dimensional Regression Coefficients by Comparison with Linearized Compressing Features, By Joachim Schaeffer et al.
Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
by Joachim Schaeffer, Jinwook Rhyu, Robin Droop, Rolf Findeisen, Richard Braatz
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Linear regression is often considered interpretable, but challenges arise when dealing with high-dimensional data. This paper explores how linear regression approximates nonlinear responses from high-dimensional functional data, motivated by predicting cycle life for lithium-ion batteries. The authors develop a linearization method to derive feature coefficients and compare them with the closest regression coefficients of the path of regression solutions. They demonstrate the methods on battery data case studies where a single compressing feature is used to construct a synthetic response. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Linear regression helps predict battery cycle life! Researchers are trying to figure out how this works when dealing with lots of data. They developed a way to make linear regression work better for high-dimensional data, which is important because batteries can be really complicated. The goal is to understand how the results change as they add more information. |
Keywords
» Artificial intelligence » Linear regression » Regression
