How do I apply feature scaling for generative models on high-dimensional data

Question

With the help of Python code, please explain to me How to apply feature scaling for generative models on high-dimensional data.

Answer 1

To apply feature scaling for generative models on high-dimensional data, you can use techniques like Min-Max Scaling, Standardization, or Robust Scaling. These ensure that the input data is normalized or standardized, which improves model training stability.

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Choose a Scaling Method:
  • Min-Max Scaling: Scales feature a fixed range, like [0, 1].
  • Standardization: Centers data to zero mean and scales to unit variance.
  • Robust Scaling: Handles outliers by scaling based on median and IQR.
• Apply Scaling: Use sklearn preprocessors or custom functions.
• Integrate with Dataset: Apply scaling within data pipelines for efficient training.

Hence, this ensures consistency across features, improves convergence, and enhances the performance of your generative model.