Manifold.MDS

Multidimensional scaling using classical MDS algorithm.

Classical MDS converts a distance matrix into a centered similarity matrix and computes its dominant eigenvectors to recover coordinates that preserve the original pairwise dissimilarities.

interface MDSOptions {
    nComponents?: number;
    dissimilarity?: 'euclidean' | 'precomputed';
}
constructor(options: MDSOptions = {})

Options

• nComponents (number, default 2): dimension of the embedded space.
• dissimilarity ('euclidean' | 'precomputed', default 'euclidean'): if 'precomputed', the input to fitTransform should be a distance matrix.

fitTransform(data: number[][]): number[][] computes the embedding and returns it.

getEmbedding(): number[][] returns the computed embedding.

Practical guide: MDS in JavaScript and TypeScript

MDS embeds points into lower dimensions while preserving pairwise distances as much as possible.

When to use MDS

• Distance geometry is more important than original feature axes.
• You need interpretable 2D/3D maps for exploratory analysis.
• You are comparing similarity relationships across entities.

Implementation workflow

1. Choose or compute a distance matrix aligned with your domain.
2. Fit MDS with target component count for visualization.
3. Validate stress/error and inspect neighborhood preservation quality.

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

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