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 …

Dimensionality reduction methods, also known as projections, are often used to explore
multidimensional data in machine learning, data science, and information visualization …

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 …

In the past few years, augmented reality (AR) and virtual reality (VR) technologies have
experienced terrific improvements in both accessibility and hardware capabilities …

Visualizations are powerful tools that are commonly used by data scientists to get more
insights about their high dimensional data. One can for example cite t-SNE, which is …

Dimensionality reduction (DR) techniques have been consistently supporting high-
dimensional data analysis in various applications. Besides the patterns uncovered by these …

Finding the similarities and differences between groups of datasets is a fundamental
analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often …

Dimensionality reduction is a common method for analyzing and visualizing high-
dimensional data. However, reasoning dynamically about the results of a dimensionality …

Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular
with visualization researchers and experienced data analysts, but present serious problems …