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 …

Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

In machine learning, pattern classification assigns high-dimensional vectors (observations)
to classes based on generalization from examples. Artificial neural networks currently …

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 …

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

Many interesting processes can be represented as time-dependent datasets. We define a
time-dependent dataset as a sequence of datasets captured at particular time steps. In such …

Constructing distributed representations for words through neural language models and
using the resulting vector spaces for analysis has become a crucial component of natural …

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

Dimensionality reduction is a compelling alternative for high-dimensional data visualization.
This method provides insight into high-dimensional feature spaces by map** relationships …