paraDime: A Framework for Parametric Dimensionality Reduction

paraDime is a modular framework for specifying and training parametric dimensionality reduction (DR) models. These models allow you to add new data points to existing low-dimensional representations of high-dimensional data. ParaDime DR models are constructed from simple building blocks (such as Relations and Relation Transforms), so that experimentation with novel DR techniques becomes easy.

Here you can see a parametric version of t-SNE [1] trained on a subset of 5000 images of handwritten digits from the MNIST dataset [2]:

The rest of the 60,000 images can then be easily embedded into the same space without retraining the t-SNE:

User Guide

• Installation
• Simple Usage
  • Importing paraDime and Loading the Dataset
  • Setting Up a Predefined Routine
  • Training the Routine and Visualizing the Results
• Building Blocks of a paraDime Routine
  • Overview
  • Dataset
  • Relations
  • Relation Transforms
  • Training Phases
  • Model
  • Losses
  • Further Information
• Examples
  • Linear Parametric MDS
  • Parametric UMAP
  • Hybrid Classification & Embedding
  • Supervised DR with a Custom Loss
  • Explainable t-SNE
  • Manual Interventions in the Latent Space

API Reference

• API Reference
  • DR
  • Relations
  • Relation Data
  • Transforms
  • Loss
  • Routines
  • Utils

References

[1]

Van Der Maaten, L., Hinton, G. “Visualizing data using t-SNE”, Journal of Machine Learning Research (2008).

[2]

LeCun, Y., Cortes, C., Burges, C.J.C. “The MNIST database of handwritten digits” (1998).