---
title: "Kernel PCA in JavaScript and TypeScript"
description: "Learn nonlinear dimensionality reduction and run the @kanaries/ml KernelPCA JavaScript implementation in browser or Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/decomposition/kernelPCA"
markdown_url: "https://ml.kanaries.net/docs/apis/decomposition/kernelPCA.md"
---
# Kernel PCA in JavaScript

## Algorithm overview

Kernel PCA applies PCA to an implicit nonlinear feature space. It can unfold curved structure that ordinary linear PCA cannot represent, using RBF, polynomial, sigmoid, cosine, or linear similarity.

## JavaScript implementation

`@kanaries/ml` provides `KernelPCA` for browser visualization and Node.js preprocessing without a Python service. It supports out-of-sample `transform`, and optional kernel-ridge pre-image reconstruction.

## Quick start example

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

const model = new Decomposition.KernelPCA({
  nComponents: 2,
  kernel: 'rbf',
  gamma: 2,
});
const embedded = model.fitTransform([[1, 0], [0, 1], [-1, 0], [0, -1]]);
console.log(embedded);
```

## Detailed API reference

```ts
new Decomposition.KernelPCA({
  nComponents?: number | null;
  kernel?: 'linear' | 'poly' | 'rbf' | 'sigmoid' | 'cosine';
  gamma?: number; degree?: number; coef0?: number;
  alpha?: number; fitInverseTransform?: boolean; randomState?: number;
})
```

Methods: `fit`, `transform`, `fitTransform`, and `inverseTransform`. Learned `eigenvalues` and `eigenvectors` are exposed as defensive copies. Kernel methods build an O(n²) training matrix, so constrain browser sample counts.