Summary of Interpreting the Curse Of Dimensionality From Distance Concentration and Manifold Effect, by Dehua Peng et al.
Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect
by Dehua Peng, Zhipeng Gui, Huayi Wu
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 investigates the “curse of dimensionality” phenomenon, where increasing dimensionality leads to decreasing performance in regression, classification, or clustering models. It identifies five challenges with manipulating high-dimensional data and attributes two major causes: distance concentration and manifold effect. The study uses nearest neighbor search and principal component analysis (PCA) to demonstrate how these causes lead to decreased model effectiveness as dimensionality increases. The findings highlight the limitations of current approaches and suggest ways to improve performance in high-dimensional spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a problem that happens when we work with very large datasets. When there are too many features, our models don’t perform well anymore. The researchers found out what causes this problem and how it makes our algorithms not work as expected. They looked at two main reasons: one is that distance measurements become less useful, and the other is that most of the information is hidden in just a few important dimensions. By understanding these challenges, we can make better models to analyze big data. |
Keywords
* Artificial intelligence * Classification * Clustering * Nearest neighbor * Pca * Principal component analysis * Regression
