ParaDime: A Framework for Parametric Dimensionality Reduction
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
CCS Concepts: Computing methodologies -> Neural networks; Learning latent representations; Human-centered computing -> Visualization systems and tools; Information visualization
@article{10.1111:cgf.14834,
journal = {Computer Graphics Forum},
title = {{ParaDime: A Framework for Parametric Dimensionality Reduction}},
author = {Hinterreiter, Andreas and Humer, Christina and Kainz, Bernhard and Streit, Marc},
year = {2023},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14834}
}