%0 Generic %1 mcinnes2018uniform %A McInnes, Leland %A Healy, John %A Melville, James %D 2018 %K cs.CG cs.LG stat.ML %T UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction %U http://arxiv.org/abs/1802.03426 %X 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.

@misc{mcinnes2018uniform, 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.}, added-at = {2021-02-22T08:57:49.000+0100}, author = {McInnes, Leland and Healy, John and Melville, James}, biburl = {https://www.bibsonomy.org/bibtex/24e07b95c8d7f645650f2c678743df036/aerover}, description = {UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}, interhash = {39e87180fe340432856512bc17149e62}, intrahash = {4e07b95c8d7f645650f2c678743df036}, keywords = {cs.CG cs.LG stat.ML}, note = {cite arxiv:1802.03426Comment: Reference implementation available at http://github.com/lmcinnes/umap}, timestamp = {2021-02-22T08:59:11.000+0100}, title = {UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}, url = {http://arxiv.org/abs/1802.03426}, year = 2018 }