Binary package “umap-learn” in ubuntu oracular
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.
Source package
Published versions
- umap-learn 0.5.4+dfsg-1 in amd64 (Proposed)
- umap-learn 0.5.4+dfsg-1 in amd64 (Release)
- umap-learn 0.5.4+dfsg-1 in arm64 (Proposed)
- umap-learn 0.5.4+dfsg-1 in arm64 (Release)
- umap-learn 0.5.4+dfsg-1 in armhf (Proposed)
- umap-learn 0.5.4+dfsg-1 in armhf (Release)
- umap-learn 0.5.4+dfsg-1 in i386 (Proposed)
- umap-learn 0.5.4+dfsg-1 in i386 (Release)
- umap-learn 0.5.4+dfsg-1 in ppc64el (Proposed)
- umap-learn 0.5.4+dfsg-1 in ppc64el (Release)
- umap-learn 0.5.4+dfsg-1 in riscv64 (Proposed)
- umap-learn 0.5.4+dfsg-1 in riscv64 (Release)
- umap-learn 0.5.4+dfsg-1 in s390x (Proposed)
- umap-learn 0.5.4+dfsg-1 in s390x (Release)