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Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction. It is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low-dimensional projection of the data that has the closest possible equivalent fuzzy topological structure. First of all UMAP is fast. It can handle large datasets and high dimensional data without too much difficulty, scaling beyond what most t-SNE packages can manage. This includes very high dimensional sparse datasets. UMAP has successfully been used directly on data with over a million dimensions. Second, UMAP scales well in the embedding dimension—it isn't just for visualization. You can use UMAP as a general-purpose dimension reduction technique as a preliminary step to other machine learning tasks.
Features
- The data is uniformly distributed on a Riemannian manifold
- Documentation available
- The Riemannian metric is locally constant (or can be approximated as such)
- The manifold is locally connected
- UMAP depends upon scikit-learn, and thus scikit-learn's dependencies
- Examples included
Project Samples
