Decomposition.TruncatedSVD

Dimensionality reduction using truncated Singular Value Decomposition of the data. Unlike PCA, the input data is not centered before decomposition.

Algorithm

The algorithm performs power iteration on the uncentered covariance matrix and keeps the top components corresponding to the largest singular values.

constructor(nComponents: number = 2)

Parameters

• nComponents (number, default 2): number of singular vectors to retain.

Methods

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

Example

const svd = new TruncatedSVD(2);
svd.fit(X);
const T = svd.transform(X_test);