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UMAP * [Manifold]

Description

Uniform Manifold Approximation and Projection (UMAP) handles nonlinear relationships and structures a bit better than PCA.

Example

from umap import UMAP

# We reduce the input embeddings from 384 dimensions to 5 dimensions
umap_model = UMAP(n_components=5, min_dist=0.0, metric="cosine", random_state=42)
reduced_embeddings = umap_model.fit_transform(embeddings)

Info

  • We can use the n_components parameter to decide the shape of the lower-dimensional space, namely 5 dimensions. Generally, values between 5 and 10 work well to capture high-dimensional global structures.
  • The min_dist parameter is the minimum distance between embedded points. We are setting this to 0 as that generally results in tighter clusters. We set metric to cosine as Euclidean-based methods have issues dealing with high-dimensional data.