Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary
input modality to direct manipulation for visual analytics can provide an engaging user …

In recent years, there has been a significant expansion in the development and use of multi-
modal sensors and technologies to monitor physical activity, sleep and circadian rhythms …

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

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 …

We present a highly effective unsupervised framework for detecting the stance of prolific
Twitter users with respect to controversial topics. In particular, we use dimensionality …

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

This paper investigates the theoretical foundations of the t-distributed stochastic neighbor
embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data …

Visual analytics systems integrate interactive visualizations and machine learning to enable
expert users to solve complex analysis tasks. Applications combine techniques from various …

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual
exploration of cluster structures of high-dimensional datasets. However, different DR …

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