Summary of Feature Space Sketching For Logistic Regression, by Gregory Dexter et al.
Feature Space Sketching for Logistic Regression
by Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas
First submitted to arxiv on: 24 Mar 2023
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 Machine learning researchers have developed novel bounds for constructing coreset models that perform logistic regression with improved efficiency. The approach, which sketches the input data, resolves open problems in previous work on coreset construction. Additionally, the team has initiated a study on forward error bounds for logistic regression and Generalized Linear Models. These findings can be applied to various applications, including feature selection and dimensionality reduction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on new ways to make computers learn faster by reducing the amount of information they need to understand. This helps with tasks like picking important features from big datasets or shrinking the size of those datasets. We’ve solved some old problems in this area and started exploring a new approach that works well for a specific type of problem called logistic regression. This can be used in many areas, including choosing what’s most important about something. |
Keywords
* Artificial intelligence * Dimensionality reduction * Feature selection * Logistic regression * Machine learning
