  ParaDime is a framework for parametric dimensionality reduction (DR). In
parametric DR, neural networks are trained to embed high-dimensional data items
in a low-dimensional space while minimizing an objective function. ParaDime
builds on the idea that the objective functions of several modern DR techniques
result from transformed inter-item relationships. It provides a common
interface for specifying these relations and transformations and for defining
how they are used within the losses that govern the training process. Through
this interface, ParaDime unifies parametric versions of DR techniques such as
metric MDS, t-SNE, and UMAP. It allows users to fully customize all aspects of
the DR process. We show how this ease of customization makes ParaDime suitable
for experimenting with interesting techniques such as hybrid
classification/embedding models and supervised DR. This way, ParaDime opens up
new possibilities for visualizing high-dimensional data.
