Skip to search formSkip to main contentSkip to account menu
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.
View PDF on arXiv
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.…
View PDF
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.
View on Springer
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.
View PDF
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.
View on IEEE
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning
TLDR
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.
Parametric UMAP Embeddings for Representation and Semisupervised Learning
TLDR
This work demonstrates that parametric UMAP performs comparably to its nonparametric counterpart while conferring the benefit of a learned parametric mapping, and explores UMAP as a regularization, constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semisupervised learning by capturing structure in unlabeled data.
View on MIT Press
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-hole…
View on Springer
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.
View on Springer
Topological Methods for Unsupervised Learning
TLDR
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.
View via Publisher
...
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License