Package: wnpp Severity: wishlist Subject: ITP: umap-learn -- Uniform Manifold Approximation and Projection Package: wnpp Owner: Andreas Tille <ti...@debian.org> Severity: wishlist
* Package name : umap-learn Version : 0.4.3 Upstream Author : Leland McInnes * URL : https://github.com/lmcinnes/umap * License : BSD-3-Clause Programming Lang: Python Description : Uniform Manifold Approximation and Projection Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t- SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data: . 1. The data is uniformly distributed on a Riemannian manifold; 2. The Riemannian metric is locally constant (or can be approximated as such); 3. The manifold is locally connected. . From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure. Remark: This package is maintained by Debian Med Packaging Team at https://salsa.debian.org/med-team/umap-learn