This talk will present a new approach to dimension reduction called UMAP. UMAP is grounded in manifold learning and topology, making an effort to preserve the topological structure of the data. The resulting algorithm can provide both 2D visualisations of data of comparable quality to t-SNE, and general purpose dimension reduction. UMAP has been implemented as a (scikit-learn compatible) python library that can perform efficient dimension reduction, scaling out to much larger datasets than t-SNE or other comparable algorithms (see http://github.com/lmcinnes/umap).
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