Tutorial 0. Introduction

[LeonieBorne]

Tutorial 0. Introduction

Would you like to participate in the writing of this tutorial? Or do you have a question about this tutorial? Let us know here!

Description

The objective of this introductory tutorial is to explain the general principles of cross-decomposition algorithms, their possible applications and practical considerations. It should introduce and refer to the other tutorials.

This tutorial should also give an overview of the different cross-decomposition algorithms that exist, including CCA, PLS regression, PLS canonical, PLS-PM (for more than 2-blocks of variables), etc.

Useful references

• Cross-decomposition in scikit-learn: scikit-learn documentation for the cross-decomposition module (CCA, PLS regression, PLS canonical). Note that the documentation should be updated soon (see current pull request, corresponding branch).
• PLS-PM: "PLS Path Modeling with R" Gaston Sanchez
• PLS-PM in Python
• PLS methods for neuroimaging: Krishnan, Anjali, et al. "Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review." Neuroimage 56.2 (2011): 455-475.
• CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)

[LeonieBorne]

We will work on this tutorial with @IsabellaBreukelaar!

[IsabellaBreukelaar]

Just created a pull request for some text I added to this tutorial, everyone feel free to improve on this as this is a my first go at interpreting these methods.