Manifold.SpectralEmbedding

Spectral embedding for non-linear dimensionality reduction using the Laplacian Eigenmaps algorithm.

The algorithm builds a nearest‑neighbor graph from the data, computes the normalized graph Laplacian and uses its leading eigenvectors (except for the trivial one) as the embedding coordinates.

interface SpectralEmbeddingProps {
    nComponents?: number;
    nNeighbors?: number;
}
constructor(props: SpectralEmbeddingProps = {})

Parameters

• nComponents (number, default 2): number of embedding dimensions.
• nNeighbors (number, default 10): how many neighbors are connected in the affinity graph.

Methods

• fit(X: number[][]): void
• fitTransform(X: number[][]): number[][]
• getEmbedding(): number[][]

Example

const se = new SpectralEmbedding({ nComponents: 2, nNeighbors: 5 });
const T = se.fitTransform(X);