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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
TLDR
The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance.
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.
Dimensionality reduction for visualizing single-cell data using UMAP
TLDR
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.
hdbscan: Hierarchical density based clustering
TLDR
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.
Accelerated Hierarchical Density Based Clustering
TLDR
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.
Manifold learning of four-dimensional scanning transmission electron microscopy
TLDR
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.
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.