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Description
This tutorial introduce to the different techniques used to evaluate/validate/select the model.
How to choose the optimal number of latent sources of variation to be extracted?
How to evaluate the contribution of each individual input variable to the overall modeling solution?
How to compare the models?
Useful references
Section 5.3 CCA for neuroscientists: Wang, Hao-Ting, et al. "Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists." NeuroImage (2020)
Section 4.6 PLS-PM: "PLS Path Modeling with R" Gaston Sanchez
Comparison CCA/PLS: Rahim, Mehdi, Bertrand Thirion, and Gaël Varoquaux. "Multi-output predictions from neuroimaging: assessing reduced-rank linear models." 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2017.
Permutation inference for CCA: Winkler, Anderson M., et al. "Permutation inference for Canonical Correlation Analysis." arXiv preprint arXiv:2002.10046 (2020).
Tutorial 3. Model selection
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
This tutorial introduce to the different techniques used to evaluate/validate/select the model.
Useful references