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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes, John Healy, James Melville, 2018. arXiv.
Introduces UMAP, a dimension-reduction method based on manifold learning and topology, widely used to lay out high-dimensional data in two or three dimensions.
What it's used for here
The inductive methods survey uses it only to display the results of anomaly detection on lines and pages (test J1).
Cited by on this site 1 record
1 methods surveys
SRCInductive methods
Cite it
McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv. https://arxiv.org/abs/1802.03426
@article{mcinnes2018umap,
author = {McInnes, Leland and Healy, John and Melville, James},
title = {UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction},
journal = {arXiv},
year = {2018},
url = {https://arxiv.org/abs/1802.03426}
}TY - GEN TI - UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction AU - McInnes, Leland AU - Healy, John AU - Melville, James PY - 2018 JO - arXiv UR - https://arxiv.org/abs/1802.03426 ER -
McInnes, Leland et al.. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.” arXiv (2018).