Summary of Making Multi-axis Gaussian Graphical Models Scalable to Millions Of Samples and Features, by Bailey Andrew et al.
Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Samples and Features
by Bailey Andrew, David R. Westhead, Luisa Cutillo
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Genomics (q-bio.GN)
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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 proposed method leverages Gaussian graphical models to uncover conditional dependencies between features in datasets, sidestepping the common assumption of sample independence. By avoiding this assumption, the approach offers improved scalability with O(n^2) runtime and O(n) space complexity, making it a viable solution for large-scale datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new method that uses Gaussian graphical models to find relationships between features in data. This is important because most current methods assume that each piece of data is independent from the others, which isn’t always true. The new approach doesn’t make this assumption and can handle big datasets more efficiently. |
