ParaDime: A Framework for Parametric Dimensionality Reduction

TL;DR

ParaDime is a flexible framework that unifies and customizes parametric dimensionality reduction techniques like t-SNE and UMAP using neural networks, enabling advanced visualization and experimentation.

Contribution

It introduces a common interface for various DR methods, allowing full customization and facilitating new hybrid and supervised DR approaches.

Findings

Unified interface for multiple DR techniques

Enables customization of DR processes

Facilitates hybrid and supervised DR experiments

Abstract

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.