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);

Practical guide: TruncatedSVD in JavaScript and TypeScript

TruncatedSVD reduces dimensionality of large sparse matrices without centering, making it practical for text/vectorized data.

When to use TruncatedSVD

• Your feature matrix is sparse (for example TF-IDF or one-hot encodings).
• You need latent semantic compression before classification or retrieval.
• PCA centering is too expensive or undesirable for sparse data.

Implementation workflow

1. Build sparse-compatible numeric input matrix.
2. Fit TruncatedSVD with candidate component counts.
3. Evaluate retrieval/classification quality on reduced features.

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

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