---
title: "Gaussian and Sparse Random Projection in JavaScript"
description: "Reduce high-dimensional data with Johnson-Lindenstrauss Gaussian or sparse random projections in JavaScript and TypeScript."
canonical_url: "https://ml.kanaries.net/docs/apis/random_projection"
markdown_url: "https://ml.kanaries.net/docs/apis/random_projection/index.html.md"
---
# Random projection in JavaScript

## Algorithm overview

Random projection reduces dimensionality with a data-independent matrix while approximately preserving pairwise distances. It is useful when deterministic decomposition is too expensive.

## JavaScript implementation

`@kanaries/ml` generates seeded Gaussian or sparse projection matrices in browser and Node.js and exposes the fitted components for serialization. Random projection is a lightweight choice when PCA-style fitting is too expensive or data arrives only at inference time; the sparse variant reduces multiply cost for high-dimensional frontend features.

## Quick start

```ts
import { RandomProjection } from '@kanaries/ml';

const X = [[1, 0, 2], [0, 1, 3], [2, 1, 0]];
const model = new RandomProjection.SparseRandomProjection({ nComponents: 128, randomState: 42 });
const reduced = model.fitTransform(X);
console.log(reduced[0].length);
```

## Detailed API reference

Set `nComponents: 'auto'` with `eps` to use the sklearn Johnson-Lindenstrauss bound. Both estimators expose `components`; `computeInverseComponents` enables approximate `inverseTransform`.