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March 31st, 2022 — Uncategorized
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March 30th, 2022 — Uncategorized
The data is uniformly distributed on a Riemannian manifold;
The Riemannian metric is locally constant (or can be approximated as such);
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.
The details for the underlying mathematics can be found in our paper on ArXiv:
McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018
The important thing is that you don’t need to worry about that—you can use UMAP right now for dimension reduction and visualisation as easily as a drop in replacement for scikit-learn’s t-SNE.
Documentation is available via Read the Docs.
New: this package now also provides support for densMAP. The densMAP algorithm augments UMAP to preserve local density information in addition to the topological structure of the data. Details of this method are described in the following paper:
Narayan, A, Berger, B, Cho, H, Density-Preserving Data Visualization Unveils Dynamic Patterns of Single-Cell Transcriptomic Variability, bioRxiv, 2020
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March 29th, 2022 — Uncategorized
This module is a TensorFlow.js GraphModel converted from the USE lite (module on TFHub), a lightweight version of the original. The lite model is based on the Transformer (Vaswani et al, 2017) architecture, and uses an 8k word piece vocabulary.
In this demo we embed six sentences with the USE, and render their self-similarity scores in a matrix
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March 27th, 2022 — Uncategorized
@paulg @erikbryn @dwallacewells @MichaelEMann
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