Let I have a Gaussian mixture consisting of $n$ Gaussians that is already fitted (e.g. using EM algorithm) with respect to a given data set. Now I want to add one more Gaussian to make the mixture more accurate.
Of course, I can start from scratch and run the same learning procedure for $n+1$ Gaussians. However, would it be possible to reuse the result from learning of $n$ Gaussians, e.g. for initialization?
I know that the $n$ Gaussians can be used somehow to form the prior for the $n+1$.
The accepted answer will contain:
- a clear way (algorithm) how to use $n$ Gaussians for learning of $n+1$ Gaussians
- some justification (explicitly stated or based on an authoritative reference) why the proposed algorithm is good.