grtlr/uapca
Uncertainty-aware principal component analysis.
This project helps data analysts and researchers understand complex datasets where each data point isn't a single value, but rather a range or distribution of possible values, like student grades with associated uncertainties. It takes in these 'fuzzy' data points (represented as probability distributions) and simplifies them into a more understandable, lower-dimensional view. The output helps you visualize the main patterns and relationships in your data while acknowledging the inherent uncertainty.
No commits in the last 6 months. Available on npm.
Use this if your data points have inherent variability or measurement error, and you need to perform dimensionality reduction to find the most important underlying patterns.
Not ideal if your data consists of precise, single-valued observations without any associated uncertainty or variability.
Stars
18
Forks
1
Language
TypeScript
License
MIT
Last pushed
Oct 13, 2021
Commits (30d)
0
Dependencies
4
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