Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMap have
demonstrated impressive visualization performance on many real-world datasets. One …

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels
for thousands of genes from up to millions of cells. Common data analysis pipelines include …

One of the most popular techniques for visualizing large, high-dimensional data sets is t-
distributed stochastic neighbor embedding (t-SNE). Recently, several extensions have been …

Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding
(t-SNE) and uniform manifold approximation and projection (UMAP), summarize the …

Dimensionality reduction, also known as manifold learning, is an area of machine learning
used for extracting informative features from data for better representation of data or …

Neighbor embeddings are a family of methods for visualizing complex high-dimensional
data sets using k NN graphs. To find the low-dimensional embedding, these algorithms …

Many approaches in machine learning rely on a weighted graph to encode thesimilarities
between samples in a dataset. Entropic affinities (EAs), which are notably used in the …

Most popular dimension reduction (DR) methods like t-SNE and UMAP are based on
minimizing a cost between input and latent pairwise similarities. Though widely used, these …

Neighbor embedding methods $ t $-SNE and UMAP are the de facto standard for visualizing
high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions …