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View Code? Open in Web Editor NEWTutorials to apply cross decomposition methods in python
License: MIT License
Tutorials to apply cross decomposition methods in python
License: MIT License
This issue contains the roadmap of this project. It's a place to start to investigate the issues that you can contribute to.
Please note that the list of tutorials proposed are by no means exhaustive. If you wish to add/modify some of them, do not hesitate to suggest it by creating a new issue!
Here is a (non-exhaustive) list of points to be dealt with before/during/after the tutorials have been written.
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.
This tutorial focus on minimal data preprocessing, usually required as for most machine-learning methods, with among other things:
This tutorial focus on dimensionality-reduction techniques (PCA, ICA, etc.) that can provide useful data preprocessing when the number of variables exceeds the number of samples.
This tutorial introduce to the different techniques used to evaluate/validate/select the model.
Would you like to participate in the writing of this tutorial?
Or do you have a question about this tutorial?
Let us know here!
This tutorial focus on minimal data preprocessing, usually required as for most machine-learning methods, with among other things:
Would you like to participate in the writing of this tutorial?
Or do you have a question about this tutorial?
Let us know here!
This tutorial introduce to the different techniques used to evaluate/validate/select the model.
Would you like to participate in the writing of this tutorial?
Or do you have a question about this tutorial?
Let us know here!
This tutorial focus on dimensionality-reduction techniques (PCA, ICA, etc.) that can provide useful data preprocessing when the number of variables exceeds the number of samples.
If people are thinking of continuing the project I am happy to advise and comment in discussions. I am wondering if we can have more people as contributors and monitor the project? @LeonieBorne will remain as the owner of the project. @nadinespy seems really keen and I do think it's great to have more people with fresh eyes.
On the practical side, I would highly recommend people adding dependencies so people can work on the same versions of libraries.
In order to write the different tutorials, we need open access databases to play with. Feel free to suggest here if you have any ideas, or to start looking for one on OpenNeuro!
Hi all,
I will add the contributors to this project during the OHBM Hackathon 2020 here.
Let's do a first test.
Would you like to participate in the writing of this tutorial?
Or do you have a question about this tutorial?
Let us know here!
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.
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