• Publications
  • Influence
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Expand
Dimensionality reduction for visualizing single-cell data using UMAP
Comparing the performance of UMAP with five other tools, it is found that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. Expand
UMAP: 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.Expand
hdbscan: Hierarchical density based clustering
HDBSCAN performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over ePSilon, which allows HDBSCAN to find clusters of varying densities, and be more robust to parameter selection. Expand
Accelerated Hierarchical Density Based Clustering
  • L. McInnes, John Healy
  • Mathematics, Computer Science
  • IEEE International Conference on Data Mining…
  • 20 May 2017
The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale parameter epsilon, making it the default choice for density based clustering. Expand
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning
It is shown that UMAP loss can be extended to arbitrary deep learning applications, for example constraining the latent distribution of autoencoders, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data. Expand
Manifold learning of four-dimensional scanning transmission electron microscopy
  • X. Li, O. Dyck, +5 authors S. Kalinin
  • Computer Science, Materials Science
  • npj Computational Materials
  • 18 October 2018
An international team using a computer protocol for data analysis, called manifold learning, to find recurrent features in a large set of images collected by illuminating an ultrathin layer of graphene with a beam of electrons, to believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials. Expand
Data-Driven Classification of Coronal Hole and Streamer Belt Solar Wind
We present two new solar wind origin classification schemes developed independently using unsupervised machine learning. The first scheme aims to classify solar wind into three types: coronal-holeExpand
Topological Methods for Unsupervised Learning
The languages of topology and category theory are used to provide a unified mathematical approach to these three major problems in unsupervised learning: dimension reduction; clustering; and anomaly detection. Expand
Author Correction: Manifold learning of four-dimensional scanning transmission electron microscopy
An amendment to this paper has been published and can be accessed via a link at the top of the paper.