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);

Practical guide: SpectralEmbedding in JavaScript and TypeScript

SpectralEmbedding uses graph Laplacian eigenvectors to reveal manifold and community structure in data.

When to use SpectralEmbedding

• Graph or affinity relationships are central to your dataset.
• You need embeddings suitable for downstream clustering.
• Non-linear structure is not captured by purely linear projections.

Implementation workflow

1. Construct an affinity graph with an appropriate similarity metric.
2. Fit SpectralEmbedding and evaluate resulting cluster separability.
3. Tune neighborhood/affinity parameters for stable embeddings.

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

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