State the limitations of Fixed Basis Function

Data Science, AI and ML
#1

Linear separability in feature space doesn’t imply linear separability in input space. So, Inputs are non-linearly transformed using vectors of basic functions with increased dimensionality. Limitations of Fixed basis functions are:

  1. Non-Linear transformations cannot remove overlap between two classes but they can increase overlap.
  2. Often it is not clear which basis functions are the best fit for a given task. So, learning the basic functions can be useful over using fixed basis functions.
  3. If we want to use only fixed ones, we can use a lot of them and let the model figure out the best fit but that would lead to overfitting the model thereby making it unstable.