Non-linear dimensionality reduction techniques, such as tSNE, are widely used to visualize
and analyze high-dimensional datasets. While non-linear projections can be of high quality …

Dimensionality reduction (DR) is a popular technique that shows great results to analyze
high-dimensional data. Generally, DR is used to produce visualizations in 2 or 3 …

High-dimensional data with many features are usually challenging to represent with
standard visualization techniques. Usually, one has to resort to dimensionality reduction …

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 (DR) is a popular approach to data exploration in which instances
in a given dataset are mapped to a lower-dimensional representation or “embedding.” For …

One first step to get insights about a dataset can be its visualization using dimensionality
reduction (DR). However, DR processes induce a loss of information that needs to be …

The prediction of the efficiency of oil well pum** systems plays an important role in
optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil …

Dimensionality reduction is often used for visualization without considering their
understanding by users. Multidimensional scaling, for instance, provides an arbitrarily …

We propose an interactive analytical system for exploring and interpreting non-linear
projections. Although non-linear projections are widely used in disclosing complex …

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