Manifold

• SpectralEmbedding
• MDS
• LocallyLinearEmbedding
• TSNE

How to use the Manifold module in real projects

The Manifold module helps project high-dimensional data into lower-dimensional embeddings for visualization, exploration, and neighborhood analysis.

Selection checklist

1. Use t-SNE for local neighborhood visualization and cluster exploration in 2D/3D.
2. Use MDS when preserving pairwise distance structure is the main requirement.
3. Use LocallyLinearEmbedding or SpectralEmbedding when manifold assumptions are meaningful for your data geometry.

Common implementation workflow

1. Start from a simple baseline in this module and evaluate on a holdout split.
2. Compare at least one alternative algorithm from this module before locking production defaults.
3. Pair model quality metrics with runtime constraints (latency, memory, bundle size).

Common search intents

• tsne javascript
• manifold learning typescript
• spectral embedding browser

Explore algorithms in this module

• MDS
• lle
• spectralEmbedding
• tsne

FAQ

What problem does Manifold solve in JavaScript machine learning projects?

Manifold helps teams implement production-ready ML workflows in browser and Node.js environments with a familiar scikit-learn-style API.

When should I choose Manifold instead of other Manifold algorithms?

Use Manifold when it best matches your data shape, labeling strategy, and runtime constraints. Benchmark against at least one alternative in the same module before finalizing defaults.

Can I run Manifold in both browser and Node.js with @kanaries/ml?

Yes. @kanaries/ml is designed for JavaScript and TypeScript runtimes across browser applications, server-side Node.js services, and edge-friendly workflows.