We propose a novel approach for preserving topological structures of the input space in
latent representations of autoencoders. Using persistent homology, a technique from …

We present new guidelines for choosing hyperparameters for t-SNE and an evaluation
comparing these guidelines to current ones. These guidelines include a proposed …

Longitudinal cohort studies to study disease progression generally combine temporal
features produced under periodic assessments (clinical follow-up) with static features …

A plethora of dimensionality reduction techniques have emerged over the past decades,
leaving researchers and analysts with a wide variety of choices for reducing their data, all …

This survey reviews and organizes existing methods for integrating constraints into
dimensionality reduction (DR). In the world of high-dimensional data, DR methods help to …

Dimensionality reduction is a commonly used technique in data analytics. Reducing the
dimensionality of datasets helps not only with managing their analytical complexity but also …

Visualizing latent embeddings is a popular approach to explain classification models,
including deep neural networks. However, existing visualization methods such as t …

Nonlinear dimensionality reduction lacks interpretability due to the absence of source
features in low-dimensional embedding space. We propose an interpretable method …

Although popularly used in big-data analytics, dimensionality reduction is a complex, black-
box technique whose outcome is difficult to interpret and evaluate. In recent years, a number …

A plethora of dimensionality reduction techniques have emerged over the past decades,
leaving researchers and analysts with a wide variety of choices for reducing their data, all …