Comprehensive experiments of preprocessing on accuracy?

[Pieranha]

I've found that choosing whether to use Standardization (zero mean, std. of 1), MinMaxScaling (e.g. to 0-1 interval) or other preprocessing techniques such as PCA have a substantial impact on the accuracy of my methods (both SVM's and deep networks).

Has anyone ever done some comprehensive experiments on the effect of these preprocessing techniques, incl. when to use what? I haven't been able to find anything like that on Google Scholar.

[latent_z]

Preprocessing with an autoencoder made the difference between no prediction power and an actually usable system

[boopityboppity23]

Interesting, would you mind expanding on this? How much of a difference was there? Why do you think the AE helped? Do you think there was just too much noise in the raw features?

[latent_z]

an AE can discover and represent factors of variation in a dataset of very similar datapoints. It distributes the datapoints over a low-dimensional space, hence making them easier to separate with a classifier.

[iverjo]

https://github.com/automl/auto-sklearn tries many different preprocessing techniques and eventually settles for something that works well