{"_id":"mnx5m88erK3NJuAca","bibbaseid":"mcinnes-healy-melville-umapuniformmanifoldapproximationandprojectionfordimensionreduction-2020","author_short":["McInnes, L.","Healy, J.","Melville, J."],"bibdata":{"bibtype":"article","type":"article","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","shorttitle":"UMAP","url":"http://arxiv.org/abs/1802.03426","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.","urldate":"2022-03-15","journal":"arXiv:1802.03426 [cs, stat]","author":[{"propositions":[],"lastnames":["McInnes"],"firstnames":["Leland"],"suffixes":[]},{"propositions":[],"lastnames":["Healy"],"firstnames":["John"],"suffixes":[]},{"propositions":[],"lastnames":["Melville"],"firstnames":["James"],"suffixes":[]}],"month":"September","year":"2020","note":"arXiv: 1802.03426","keywords":"Computer Science - Computational Geometry, Computer Science - Machine Learning, Statistics - Machine Learning","bibtex":"@article{mcinnes_umap_2020,\n\ttitle = {{UMAP}: {Uniform} {Manifold} {Approximation} and {Projection} for {Dimension} {Reduction}},\n\tshorttitle = {{UMAP}},\n\turl = {http://arxiv.org/abs/1802.03426},\n\tabstract = {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.},\n\turldate = {2022-03-15},\n\tjournal = {arXiv:1802.03426 [cs, stat]},\n\tauthor = {McInnes, Leland and Healy, John and Melville, James},\n\tmonth = sep,\n\tyear = {2020},\n\tnote = {arXiv: 1802.03426},\n\tkeywords = {Computer Science - Computational Geometry, Computer Science - Machine Learning, Statistics - Machine Learning},\n}\n\n\n\n\n\n\n\n","author_short":["McInnes, L.","Healy, J.","Melville, J."],"key":"mcinnes_umap_2020","id":"mcinnes_umap_2020","bibbaseid":"mcinnes-healy-melville-umapuniformmanifoldapproximationandprojectionfordimensionreduction-2020","role":"author","urls":{"Paper":"http://arxiv.org/abs/1802.03426"},"keyword":["Computer Science - Computational Geometry","Computer Science - Machine Learning","Statistics - Machine Learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["computer science - computational geometry","computer science - machine learning","statistics - machine learning"],"search_terms":["umap","uniform","manifold","approximation","projection","dimension","reduction","mcinnes","healy","melville"],"title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","year":2020}