EVRI-thing You Need to Know About t-SNE & UMAP in PLS_Toolbox/Solo

PLS_Toolbox and Solo version 9.0 include two relatively new data visualization methods, t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). These routines, from the scikitlearn library for Python, can now be found in our interfaces (and for PLS_Toolbox, also at the MATLAB® command line). We will introduce these methods and show how to access them in the new PLS_Toolbox and Solo.

Table of Contents

00:00 – Introduction and Outline

03:00 – A Brief Word about PLS_Toolbox/Solo

04:24 – Python Integration

05:51 – Introduction to t-SNE

14:10 – Examples

30:45 – Introduction to UMAP

39:15 – Examples

1:09:05 – Online Resources

TSNE Learning Resources

• StatQuest, Josh Starmer
  • https://www.youtube.com/watch?v=NEaUSP4YerM

• Sklearn user guide
  • https://scikit-learn.org/stable/modules/manifold.html#t-sne

• Sklearn documentation
  • https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html

• How to use t-SNE effectively
  • https://distill.pub/2016/misread-tsne/Understanding UMAP by Andy Coenen and Adam Pearce

UMAP Learning Resources

• McInnes presentation at SciPy 2018
  • https://www.youtube.com/watch?v=nq6iPZVUxZU

• UMAP explained by Letitia Parcalabescu, AI Coffee Break
  • https://www.youtube.com/watch?v=6BPl81wGGP8

• How UMAP Works
  • https://umap-learn.readthedocs.io/en/latest/how_umap_works.html

• A Practical Guide to UMAP by Healy
  • https://bioturing.com/sources/webinar/615418b14d854d1f1d362b37/a-practical-guide-to-umap

• Understanding UMAP by Andy Coenen and Adam Pearce
  • https://pair-code.github.io/understanding-umap/