Summary of Enhancing Dimension-reduced Scatter Plots with Class and Feature Centroids, by Daniel B. Hier et al.
Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids
by Daniel B. Hier, Tayo Obafemi-Ajayi, Gayla R. Olbricht, Devin M. Burns, Sasha Petrenko, Donald C. Wunsch II
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 This paper presents a solution to improve the interpretability of two-dimensional biomedical data, which is often reduced using dimension reduction techniques. The authors highlight that the resulting x and y coordinates can be complex to understand, making it difficult to analyze patterns in the data. To address this challenge, they propose a method that uses these coordinates to calculate class and feature centroids, which can be overlaid onto scatter plots. This approach enables connections between the low-dimensional space and the original high-dimensional space, increasing the interpretability of the results. The authors demonstrate the effectiveness of their method using data from three neurogenetic diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sense of medical data that’s been shrunk down to two dimensions. When we do this, each piece of data gets an x and y coordinate, like a point on a map. But it can be hard to figure out what the x and y axes really mean. The authors came up with a way to use these coordinates to calculate special points that help us understand the data better. They showed how this works using real-world medical data from three diseases that affect the brain. |
