Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning

no code implementations • 28 Sep 2020 • Tim Sainburg, Leland McInnes, Timothy Q Gentner

We propose Parametric UMAP, a parametric variation of the UMAP (Uniform Manifold Approximation and Projection) algorithm.

Dimensionality Reduction

Parametric UMAP embeddings for representation and semi-supervised learning

2 code implementations • 27 Sep 2020 • Tim Sainburg, Leland McInnes, Timothy Q Gentner

UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data.

Dimensionality Reduction

Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

1 code implementation • 18 Oct 2018 • Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse, Sergei V. Kalinin

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

37 code implementations • 9 Feb 2018 • Leland McInnes, John Healy, James Melville

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.

BIG-bench Machine Learning Dimensionality Reduction

Accelerated Hierarchical Density Clustering

3 code implementations • 20 May 2017 • Leland McInnes, John Healy

We present an accelerated algorithm for hierarchical density based clustering.

Clustering