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 …

Deep neural networks are now rivaling human accuracy in several pattern recognition
problems. Compared to traditional classifiers, where features are handcrafted, neural …

Multidimensional projection techniques have experienced many improvements lately,
mainly regarding computational times and accuracy. However, existing methods do not yet …

Designing a complete visualization system involves many subtle decisions. When designing
a complex, real-world visualization system, such decisions involve many types of …

In recent years, dimensionality‐reduction techniques have been developed and are widely
used for hypothesis generation in Exploratory Data Analysis. However, these techniques are …

Digital methods are increasingly applied to store, structure and analyse vast amounts of
musical data. In this context, visualization plays a crucial role, as it assists musicologists and …

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

Data-driven methods serve an increasingly important role in discovering geometric,
structural, and semantic relationships between shapes. In contrast to traditional approaches …