Manifold.LocallyLinearEmbedding

Locally Linear Embedding (LLE) reconstructs each sample from its nearest neighbors and finds a low‑dimensional representation that preserves these local relationships.

constructor(
    nNeighbors: number = 5,
    nComponents: number = 2,
    reg: number = 0.001
)

Parameters

• nNeighbors (number, default 5): how many neighbors to use for the local reconstructions.
• nComponents (number, default 2): dimension of the returned embedding.
• reg (number, default 0.001): regularization value added to the covariance matrix to ensure numerical stability.

Methods

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

Example

const lle = new LocallyLinearEmbedding(5, 2);
const Y = lle.fitTransform(X);

Practical guide: LocallyLinearEmbedding in JavaScript and TypeScript

LocallyLinearEmbedding captures manifold structure by preserving local linear neighborhoods in a low-dimensional embedding.

When to use LocallyLinearEmbedding

• Data lies on a non-linear manifold with meaningful local geometry.
• You want neighborhood-preserving visualization or preprocessing.
• Linear projection methods (like PCA) lose important local structure.

Implementation workflow

1. Select neighborhood size based on expected local manifold smoothness.
2. Fit and inspect embedding quality visually and with neighborhood metrics.
3. Tune neighbors/components to balance stability and detail.

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

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