https://github.com/grtlr/uapca

Uncertainty-aware principal component analysis.
https://github.com/grtlr/uapca

dimensionality-reduction hacktoberfest machine-learning uncertainty visualization

Last synced: 2 months ago
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Uncertainty-aware principal component analysis.

• Host: GitHub
• URL: https://github.com/grtlr/uapca
• Owner: grtlr
• License: mit
• Created: 2019-08-08T15:32:11.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-10-13T07:40:36.000Z (over 3 years ago)
• Last Synced: 2024-10-05T09:09:57.184Z (3 months ago)
• Topics: dimensionality-reduction, hacktoberfest, machine-learning, uncertainty, visualization
• Language: TypeScript
• Homepage: https://arxiv.org/abs/1905.01127
• Size: 823 KB
• Stars: 17
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# Uncertainty-aware principal component analysis

![Build Status](https://github.com/grtlr/uapca/workflows/build/badge.svg)

[![npm](https://img.shields.io/npm/v/uapca)](https://www.npmjs.com/package/uapca)

![GitHub](https://img.shields.io/github/license/grtlr/uapca)

This is an implementation of uncertainty-aware principal component analysis, which generalizes PCA to work on probability distributions. You can find a live Observable notebook demonstrating our method [here](https://observablehq.com/@grtlr/uncertainty-aware-pca).

![Teaser](https://raw.githubusercontent.com/grtlr/uapca/master/teaser.gif)

You can find a preprint of our paper at [arXiv:1905.01127](https://arxiv.org/abs/1905.01127) or on my [personal website](https://www.jgoertler.com).

We also extracted means and covariances from the [*student grades* dataset](https://raw.githubusercontent.com/grtlr/uapca/master/data/student_grades.json).

## Example

```js

// Loading the library

import * as uapca from 'uapca';

// Loading and converting the dataset

const student_grades = (await fetch('https://raw.githubusercontent.com/grtlr/uapca/master/data/student_grades.json')).json();

const distributions = student_grades.distributions.map(d => new uapca.MultivariateNormal(d.mean, d.cov));

// Perform uncertainty-aware PCA with scaling factor k = 0.5

const pca = uapca.UaPCA.fit(distributions, 0.5).aligned();

// Project the data onto 2D

const projected_distributions = pca.transform(distributions, 2);

```

## Development

The dependencies can be install using `yarn`:

```bash

yarn install

```

Builds can be prepared using:

```bash

yarn run build

yarn run dev # watches for changes

```

Run tests:

```bash

yarn run test

```

    

To perform linter checks you there is:

```bash

yarn run lint

yarn run lint-fix # tries to fix some of the warnings

```

## Citation

To cite this work, you can use the BibTex entry in `CITATION.cff`.