Featuring Developer: Leland McInnes
Air Date 11 October 2019
@12 PM Eastern
We will be joined by Leland McInnes, developer on the UMAP project, who will tell us about the future of UMAP. 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
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.
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.