Linear Discriminant Analysis (LDA) before or after k-fold cross-validation? I have features extracted from a small dataset, would like to reduce the dimensions by using LDA. Also want to do a SVM classification with k-fold cross-validation. 

My question is: What would be the best practice: to do LDA before the CV, or to do LDA within the CV (i.e. to each train and test fold)?
It depends on what you want to achieve.

If you want to visualize the results of your SVM classification, then you should do it after.

If you want to reduce noise, speed up training ... or whatever reason you want to reduce the dimensionality of your problem. You should do it before. 

One idea here that can be useful, is to do LDA inside a pipeline and choosing the best hyperparameter within the CV. 

In this example you can check a PCA and then a logistic regression. Your case will be similar but with LDA and SVM.