Summary of On High-dimensional Modifications Of the Nearest Neighbor Classifier, by Annesha Ghosh et al.
On high-dimensional modifications of the nearest neighbor classifier
by Annesha Ghosh, Deep Ghoshal, Bilol Banerjee, Anil K. Ghosh
First submitted to arxiv on: 6 Jul 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 paper proposes novel methods to improve the performance of nearest neighbor classifiers in high-dimensional, low-sample size (HDLSS) situations. Nearest neighbor classifiers are simple yet popular nonparametric classification algorithms, but they often struggle when dealing with HDLSS data. This is because HDLSS data exhibits concentration of pairwise distances and violation of neighborhood structure, making it challenging for the classifier to distinguish between classes. The authors discuss existing methods that attempt to address this issue and propose new ones. They also conduct theoretical investigations and analyze simulated and benchmark datasets to compare the empirical performances of proposed methods with existing ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make a simple classification method work better when there’s not much data. This method, called nearest neighbor classifier, is easy to use but often doesn’t do well when dealing with lots of features (high-dimensional) and not many samples (low-sample size). The authors talk about some existing solutions to this problem and come up with new ideas. They also test their methods on real and fake data to see how they compare. |
Keywords
* Artificial intelligence * Classification * Nearest neighbor
