It might be a beginner question, but I'm not sure how to normalize my data.
Let's suppose I have a NxM matrix with N samples of M dimensions each. If I want to normalize my data I can do it in two ways:
Samplewise: I take each sample and normalize it's features such as the end up being a unit vector (L2) or they just sum 1 (L1)
Featurewise: I take each feature and normalize it's values across all samples.
The problem I see is that in both cases I will end up loosing some relationship information.
Let's see an example:
Subject_1 180 20
Subject_2 190 40
If I normalize rowwise:
Subject_1 180/200 = 0.9 20/200 = 0.1
Subject_2 190/250 = 0.76 40/250 = 0.16
Here you can see that even if the Subject_1 is shorter than the subject_2, when normalizing subject_2 ends up being taller (since my normalization is independent between samples)
If I normalize columnwise:
Subject_1 180/370 = 0.49 20/60 = 0.33
Subject_2 190/370 = 0.51 40/60 = 0.67
Here I can see that even if subject_2 has a way lower value for arm_length than height, it ends up with a higher value for arm_length than height (0.67 vs 0.51)
Also normalizing I loose the absolute values and end up only with relationships.
Image a system that depends not only on the absolute height and arm_length but also in the relationship between them.
So basically my question is: Should I normalize at all? If yes, columnwise, or rowwise?
Also, would it be a good idea to normalize both ways and append both into a new 2*M dimensional feature vector?
The relationship between features is definetely important. Imagine a system where different body shapes behave differently, in such case a relation between Chest feature and Waist feature will be extremely important.
By normalizing featurewise I'll loose this relationship.