---
title: "Incremental PCA in JavaScript and TypeScript"
description: "Process numeric data in batches with the @kanaries/ml IncrementalPCA JavaScript implementation for memory-aware browser and Node.js workflows."
canonical_url: "https://ml.kanaries.net/docs/apis/decomposition/incrementalPCA"
markdown_url: "https://ml.kanaries.net/docs/apis/decomposition/incrementalPCA.md"
---
# Incremental PCA in JavaScript

## Algorithm overview

Incremental PCA updates a low-rank SVD one batch at a time. It is useful when a complete dataset should not be retained in memory, while still producing PCA-style projections and inverse transforms.

## JavaScript implementation

`@kanaries/ml` follows sklearn's mean-corrected incremental SVD update. Call `partialFit` for streamed batches, or `fit` to let the estimator split an in-memory matrix by `batchSize`.

## Quick start example

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

const batchOne = [[0, 1, 2], [1, 2, 3], [2, 1, 4]];
const batchTwo = [[3, 4, 2], [4, 3, 1], [5, 5, 0]];
const nextBatch = [[2, 3, 2]];
const pca = new Decomposition.IncrementalPCA({ nComponents: 2 });
pca.partialFit(batchOne).partialFit(batchTwo);
const embedding = pca.transform(nextBatch);
console.log(embedding);
```

## Detailed API reference

```ts
new Decomposition.IncrementalPCA({ nComponents?: number | null, batchSize?: number })
```

- `partialFit(X: number[][]): this` updates the retained SVD.
- `fit(X: number[][]): void` resets and processes batches.
- `transform` and `inverseTransform` project data.
- `components`, `mean`, `explainedVariance`, `singularValues`, and `nSamplesSeen` expose learned state.

The first `partialFit` batch must contain at least `nComponents` samples and features.