[ ソース: umap-learn ]
パッケージ: umap-learn (0.5.4+dfsg-1)
umap-learn に関するリンク
Debian の資源:
umap-learn ソースパッケージをダウンロード:
- [umap-learn_0.5.4+dfsg-1.dsc]
- [umap-learn_0.5.4+dfsg.orig.tar.xz]
- [umap-learn_0.5.4+dfsg-1.debian.tar.xz]
メンテナ:
外部の資源:
- ホームページ [github.com]
類似のパッケージ:
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.
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