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);