https://github.com/hyu-kim/mds-hypothesis-testing
Multidimensional scaling method for F-informed hypothesis testing
https://github.com/hyu-kim/mds-hypothesis-testing
multivariate-statistics
Last synced: 4 months ago
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Multidimensional scaling method for F-informed hypothesis testing
- Host: GitHub
- URL: https://github.com/hyu-kim/mds-hypothesis-testing
- Owner: hyu-kim
- License: mit
- Created: 2022-07-11T04:15:20.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-12-24T00:57:54.000Z (5 months ago)
- Last Synced: 2025-12-25T13:44:57.337Z (5 months ago)
- Topics: multivariate-statistics
- Language: HTML
- Homepage:
- Size: 65.6 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# mds-hypothesis-testing
- Multidimensional scaling method for _F_-informed hypothesis testing.
- Paper link = https://arxiv.org/abs/2308.00354
### Summary
>Multidimensional scaling (MDS) is an unsupervised learning technique that preserves pairwise distances between observations and is commonly used for analyzing multivariate biological datasets. Recent advances in MDS have achieved successful classification results, but the configurations heavily depend on the choice of hyperparameters, limiting its broader application. Here, we present a self-supervised MDS approach informed by the dispersions of observations that share a common binary label (_F_-ratio). Our visualization accurately configures the _F_-ratio while consistently preserving the global structure with a low data distortion compared to existing dimensionality reduction tools. Using an algal microbiome dataset, we show that this new method better illustrates the community's response to the host, suggesting its potential impact on microbiology and ecology data analysis.