High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …

Trait‐based ecology aims to understand the processes that generate the overarching
diversity of organismal traits and their influence on ecosystem functioning. Achieving this …

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 …

Dimensionality reduction methods, also known as projections, are frequently used in
multidimensional data exploration in machine learning, data science, and information …

UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied
Riemannian geometry and algebraic topology to find low-dimensional embeddings of …

Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

Visual analysis of multidimensional data requires expressive and effective ways to reduce
data dimensionality to encode them visually. Multidimensional projections (MDP) figure …

We propose a novel approach for preserving topological structures of the input space in
latent representations of autoencoders. Using persistent homology, a technique from …

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of
multidimensional data has proven to be a popular approach, with successful applications in …

Data of different levels of complexity and of ever growing diversity of characteristics are the
raw materials that machine learning practitioners try to model using their wide palette of …