Embedding is a common technique for analyzing multi-dimensional data. However, the
embedding projection cannot always form significant and interpretable visual structures that …

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

Building an effective Machine Learning (ML) model for a data set is a difficult task involving
various steps. One of the most important steps is to compare a substantial amount of …

We present a set of interactive visual analysis techniques aiming at explaining data patterns
in multidimensional projections. Our novel techniques include a global value-based …

Cluster interpretation after dimensionality reduction (DR) is a ubiquitous part of exploring
multidimensional datasets. DR results are frequently represented by scatterplots, where …

Projections are well-known techniques that help the visual exploration of high-dimensional
data by creating depictions thereof in a low-dimensional space. While projections that target …

Understanding how a machine learning classifier works is an important task in machine
learning engineering. However, doing this is for any classifier in general difficult. We …

While 2D projections are established tools for exploring high-dimensional data, the
effectiveness of their 3D counterparts is still a matter of debate. In this work, we address this …

Inverse projection techniques and decision maps are recent tools proposed to depict the
behavior of a classifier using 2D visualizations. However, which parts of the large, high …

We present a set of interactive visual analysis techniques aiming at explaining data patterns
in multidimensional projections. Our novel techniques include a global value-based …