arXiv (Cornell University)· 2018· Preprint
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- 7,581citations
- 2018year
Short summary
UMAP, a new manifold learning technique, reduces data dimensions with competitive visualization quality to t-SNE but preserves more global structure and runs faster.
AI-generated from the title and abstract; the full text is not read.
Key points
- UMAP is a new manifold learning technique for dimension reduction.
- It is constructed from theoretical frameworks in Riemannian geometry and algebraic topology.
- UMAP offers competitive visualization quality with t-SNE.
- It preserves more global structure and has superior runtime performance compared to t-SNE.
- UMAP has no computational restrictions on embedding dimension, enabling general-purpose use.
AI-generated from the title and abstract; the full text is not read.
Abstract
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
The authors' abstract, as published at the source. arXiv (Cornell University), 2018 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science