Decomposition.PCA

Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD.

Algorithm

The covariance matrix of the centered samples is decomposed by power iteration. The resulting eigenvectors form the principal components sorted by explained variance.

constructor(nComponents: number | null = null)

Parameters

• nComponents (number | null, default null): number of principal components to retain. If null, all components are used.

Methods

• fit(X: number[][]): void
• transform(X: number[][]): number[][]
• fitTransform(X: number[][]): number[][]
• inverseTransform(X: number[][]): number[][]
• getComponents(): number[][]
• getMean(): number[]
• getExplainedVariance(): number[]

Example

const pca = new PCA(2);
pca.fit(X);
const T = pca.transform(X_test);

Practical guide: PCA in JavaScript and TypeScript

PCA compresses correlated numeric features into orthogonal components while preserving maximal variance.

When to use PCA

• Feature dimensions are high and training speed needs improvement.
• You need 2D/3D projections for exploratory visualization.
• Multicollinearity hurts stability in downstream supervised models.

Implementation workflow

1. Standardize continuous features before decomposition.
2. Fit PCA and select component count by explained variance.
3. Use transformed features for visualization or downstream training.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

• PCA JavaScript
• PCA TypeScript
• PCA browser machine learning
• @kanaries/ml PCA