Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

In this paper, we present a systematization of techniques that use quality metrics to help in
the visual exploration of meaningful patterns in high-dimensional data. In a number of recent …

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

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 …

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

Inspired by the great success of machine learning (ML), researchers have applied ML
techniques to visualizations to achieve a better design, development, and evaluation of …

Traditional scatterplots fail to scale as the complexity and amount of data increases. In
response, there exist many design options that modify or expand the traditional scatterplot …

Various case studies in different application domains have shown the great potential of
visual parameter space analysis to support validating and using simulation models. In order …

Labeling data instances is an important task in machine learning and visual analytics. Both
fields provide a broad set of labeling strategies, whereby machine learning (and in particular …