---
title: "Non-negative Matrix Factorization (NMF) in JavaScript"
description: "Factor non-negative data into additive parts using the @kanaries/ml NMF JavaScript and TypeScript implementation in browser or Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/decomposition/nmf"
markdown_url: "https://ml.kanaries.net/docs/apis/decomposition/nmf.md"
---
# NMF in JavaScript

## Algorithm overview

Non-negative Matrix Factorization approximates `X ≈ W × H` while keeping every factor non-negative. The additive representation is useful for topic features, parts-based image representations, and interpretable latent components.

## JavaScript implementation

`@kanaries/ml` uses multiplicative Frobenius-loss updates and supports random and NNDSVD initializations. It runs in browser or Node.js and exposes both learned components and reconstruction error.

## Quick start example

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

const nmf = new Decomposition.NMF({ nComponents: 2, init: 'nndsvda', randomState: 0 });
const W = nmf.fitTransform([[1, 2, 0], [0, 1, 3], [1, 3, 3]]);
const approximation = nmf.inverseTransform(W);
console.log({ W, approximation });
```

## Detailed API reference

Options: `nComponents`, `init: 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar'`, `maxIter`, `tol`, `alphaW`, `alphaH`, `l1Ratio`, and `randomState`.

Methods: `fit`, `transform`, `fitTransform`, `inverseTransform`. Learned fields: `components`, `reconstructionErr`, and `nIter`. Input must be a finite, rectangular, non-negative matrix.