Answers.Body: <p>There is, beside of numerical <em>losses</em> not difference between directly using z-normalization and first min-max and then z-normalization.</p>
  <h5>Explanation</h5>
  <p>Both are affine transformations and a combination of affine transformations is again an affine transformation. Since there is only one(*) affine transformation that results on zero-mean and variance of one, the z-normalization will (kind of) undo all previous affine transformations to achieve this goal.</p>
  <p>(*): In fact there are two and you could get the other by multiplying every value with -1. But this is just a silly detail and does not matter for this question.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-26T21:58:00.300Z
Answers.LastActivityDate: 2023-06-26T21:58:00.300Z
Answers.UserDisplayName: Broele
Body: <p>I want to implement a neural network in Pytorch for medical image segmentation. I should normalise my data.</p>
  <p>Should I apply a min-max scale (range 0 to 1) before applying the normalisation or should I apply the z-score normalisation directly?</p>
  <ol>
  <li><p>What is the best approach if the dataset comes from a single source of data?</p>
  </li>
  <li><p>What if the samples come from multiple sources?</p>
  </li>
  </ol>
  
Tags: <machine-learning><deep-learning><data-preprocessing><image-segmentation><normalisation>
Title: Should I apply a min-max scale (range 0 to 1) before applying the normalisation or should I apply the z-score normalisation directly?
UserDisplayName: Janikas
ThreadText: # Should I apply a min-max scale (range 0 to 1) before applying the normalisation or should I apply the z-score normalisation directly?
  
  (Asked by: username_0 on 2023-06-26)
  
  I want to implement a neural network in Pytorch for medical image segmentation. I should normalise my data.
  
  Should I apply a min-max scale (range 0 to 1) before applying the normalisation or should I apply the z-score normalisation directly?
  
  1. What is the best approach if the dataset comes from a single source of data?
  2. What if the samples come from multiple sources?
  
  **Tags:** machine-learning, deep-learning, data-preprocessing, image-segmentation, normalisation
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-06-26. Score: 2
  
  There is, beside of numerical *losses* not difference between directly using z-normalization and first min-max and then z-normalization.
  
  ##### Explanation
  
  Both are affine transformations and a combination of affine transformations is again an affine transformation. Since there is only one(\*) affine transformation that results on zero-mean and variance of one, the z-normalization will (kind of) undo all previous affine transformations to achieve this goal.
  
  (\*): In fact there are two and you could get the other by multiplying every value with -1. But this is just a silly detail and does not matter for this question.
  
  
  ---
  
  