---
title: "Latent Dirichlet Allocation Topic Modeling in JavaScript"
description: "Discover document topics from dense or CSR word-count matrices in JavaScript and TypeScript with @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/decomposition/latentDirichletAllocation"
markdown_url: "https://ml.kanaries.net/docs/apis/decomposition/latentDirichletAllocation.md"
---
# Latent Dirichlet Allocation in JavaScript

## Algorithm overview

LDA models each document as a mixture of latent topics and each topic as a distribution over words. It is useful for exploratory topic discovery after count vectorization.

## JavaScript implementation

`LatentDirichletAllocation` accepts dense or `CSRMatrix` non-negative counts and performs batch variational updates entirely in browser or Node.js.

## Quick start

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

const documents = ['cats chase mice', 'dogs chase balls', 'cats and dogs'];
const counts = new FeatureExtraction.CountVectorizer().fitTransform(documents);
const topics = new Decomposition.LatentDirichletAllocation({ nComponents: 5, randomState: 0 }).fitTransform(counts);
console.log(topics);
```

## Detailed API reference

Options include topic priors, `maxIter`, `maxDocUpdateIter`, and `meanChangeTol`. `components` contains unnormalized topic-word parameters.