How to Normalise features for small datasets? I am working with a small dataset ( N  = 50 ).
I would like to normalise  my input features. 
I am facing the following issues:


Because of the small size of the dataset the range of training input features would differ from the range of testing input features. 
The input features do not have a theoretical upper bound.


Can you suggest normalisation techniques suitable for this task? Any paper suggestions would also be appreciated.
You can use a MinMaxScaler on your train set, which will normalize your features inside [0, 1]. The same scaler can transform the test set and if there are values greater than the ones found in the train set, the scaler handles that by returning values greater than 1. Essentially, your test set will be normalized.