I am very new to ML and have limited knowledge about it. I am having issue in feature normalization process. I have understood from the post that we need to normalize the training features and scale the test/validation features with the training data. I am facing issue in the implementation as in my case my training samples have fixed dimension but the dimension of validation and test data is variable. So, I can apply zero mean unit variance for training data but I am not sure how can I normalize the validation/test data samples as the sample dimension/length is variable/not fixed.
That is a common case on image and audio processing, you need to find a way in which dimensions stay the same, such as normalizing per channel.
If you have a 1D vector of features, taking mean and variance of all variables will end up normalizing it in a way, it works in Computer Vision like a charm. It is also a way to reduce the space cost of your normalizing algorithm.
The easiest way is to pad your data into the same length. For example make all training, validation, & test subjects into the same length by add 0 at the end or beginning of each subject, then your problem should be solved. You can refer to this keras example for a better idea.