State the limitations of Fixed Basis Function.

4 years ago
Machine Learning

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:

  • Non-Linear transformations cannot remove overlap between two classes but they can increase overlap.
  • 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.
  • 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. 
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Sanisha Maharjan
Sanisha Maharjan
Jan 11, 2022
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