ParaDime: A Framework for Parametric Dimensionality Reduction
This is an incremental contribution that provides a flexible tool for researchers and practitioners working on data visualization and dimensionality reduction.
The authors tackled the problem of unifying parametric dimensionality reduction techniques by introducing ParaDime, a framework that provides a common interface for customizing and experimenting with methods like metric MDS, t-SNE, and UMAP, enabling new applications such as hybrid classification/embedding models and supervised DR.
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.