Hello,
I am dealing with a regression problem where inputs are images 98x98, and outputs are vectors of 16 elements.
Some examples of the outputs are:
[12023, 0.1, 2.0, 11982, 0.8, 1.2, 0.3, 0.9, 1.9, 1.1, 0.4, 0.5, 1.0, 0.9, 0.9, 1.7]
[11975, 0.6, 2.1, 11145, 0.4, 1.1, 0.9, 0.2, 1.3, 1.6, 0.1, 0.4, 1.5, 0.4, 0.8, 1.0]
etc.
As you can see the first and the fourth elements are several orders of magnitudes larger than the rest of the vectors.
The question is if this going to affect the learning process negatively and if labels need to be preprocessed somehow (e.g. normalized, or something else)?