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UMAP

Also known as: UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction

OriginatorMcInnes, L.; Healy, J.; Melville, J.Year2018Sources1Related methods6

UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.

Key highlights

  • Fast and scalable to large datasets, far quicker than t-SNE.
  • Preserves global structure better than t-SNE while still capturing local clusters.
  • Captures nonlinear structure that linear methods such as PCA cannot.
  • Produces 2- or 3-dimensional embeddings well suited to visualisation.

Intuition

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How it works

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When to use it

Use UMAP to visualise or compress continuous, high-dimensional data on at least about 50 observations, whether for exploration or as a preprocessing step before classification. The n_neighbors and min_dist hyperparameters should be tuned to the structure you care about, and a random_state should be fixed for reproducibility. Below about 50 observations the manifold cannot be learned reliably and linear PCA is the safer choice.

Strengths & limitations

Strengths
  • Fast and scalable to large datasets, far quicker than t-SNE.
  • Preserves global structure better than t-SNE while still capturing local clusters.
  • Captures nonlinear structure that linear methods such as PCA cannot.
  • Produces 2- or 3-dimensional embeddings well suited to visualisation.
Limitations
  • Distances between clusters in the embedding can be misleading and should not be over-interpreted.
  • Results depend on the n_neighbors and min_dist hyperparameters, which must be tuned.
  • Output is not deterministic unless a random_state is fixed.
  • On small samples (n below about 50) the manifold cannot be learned reliably and the visual becomes misleading; PCA is preferable.

Common pitfalls

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Applications

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Frequently asked

How is UMAP different from t-SNE?

Both are nonlinear methods for visualising high-dimensional data, but UMAP is built on manifold-learning theory, tends to preserve global structure better, and is much faster on large datasets.

Can I interpret distances between clusters in the plot?

Be cautious. UMAP preserves cluster shapes, but the distances between clusters can be misleading and should not be read as precise quantities.

Which hyperparameters matter most?

n_neighbors controls the balance between local and global structure, and min_dist controls how tightly points pack together. Both should be tuned, and a random_state should be fixed so the embedding is reproducible.

What if I have very few observations?

Below about 50 observations the manifold cannot be learned reliably and the embedding becomes misleading. In that case a linear method such as PCA is the safer choice.

Sources

  1. 1.
    McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.
    https://arxiv.org/abs/1802.03426

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ScholarGate. (2026, June 1). UMAP. ScholarGate. https://scholargate.app/machine-learning/umap-reduction