  Dimensionality reduction (DR) is a popular method for preparing and analyzing
high-dimensional data. Reduced data representations are less computationally
intensive and easier to manage and visualize, while retaining a significant
percentage of their original information. Aside from these advantages, these
reduced representations can be difficult or impossible to interpret in most
circumstances, especially when the DR approach does not provide further
information about which features of the original space led to their
construction. This problem is addressed by Interpretable Machine Learning, a
subfield of Explainable Artificial Intelligence that addresses the opacity of
machine learning models. However, current research on Interpretable Machine
Learning has been focused on supervised tasks, leaving unsupervised tasks like
Dimensionality Reduction unexplored. In this paper, we introduce LXDR, a
technique capable of providing local interpretations of the output of DR
techniques. Experiment results and two LXDR use case examples are presented to
evaluate its usefulness.
