High-dimensional data are a key study object for both machine learning (ML) and
information visualization. On the visualization side, dimensionality reduction (DR) methods …

Lacking supervised data is an issue while training deep neural networks (DNNs), mainly
when considering medical and biological data where supervision is expensive. Recently …

We present a method to visually assess the stability of deep learned projections. For this, we
perturb the high-dimensional data by controlled sequences and visualize the resulting …

Self-Supervised Network Projections (SSNP) are dimensionality reduction algorithms that
produce lowdimensional layouts from high-dimensional data. By combining an autoencoder …

Dimensionality reduction (DR) methods, also called projections, are one of the techniques of
choice for visually exploring large high-dimensional datasets. In parallel, machine learning …

Dimensionality reduction (DR) methods create 2D scatterplots of high-dimensional data for
visual exploration. As such scatterplots are often used to reason about the cluster structure …

Understanding how data separation (DS), visual separation (VS), and classifier performance
(CP) are related to each other is important for applications in both machine learning and …

Source detection and taxonomy of celestial objects are key steps in any astronomical
analysis. Examples of this include the classification of stars based on their spectral …