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

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data.
For DR techniques to be useful in exploratory data analysis, they need to be adapted to …

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 …

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual
cluster analysis. Although dimensionality reduction of high-dimensional data is widely used …

The assignment of labels to data instances is a fundamental prerequisite for many machine
learning tasks. Moreover, labeling is a frequently applied process in visual interactive …

Dimension reduction algorithms and clustering algorithms are both frequently used
techniques in visual analytics. Both families of algorithms assist analysts in performing …

The labeling of data sets is a time‐consuming task, which is, however, an important
prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) …

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 …

Projection techniques are often used to visualize high-dimensional data, allowing users to
better understand the overall structure of multi-dimensional spaces on a 2D screen …