If you look at the tail-end of
http://social.microsoft.com/Forums/en-US/infer.net/thread/1be040b0-5466-487b-93e8-d2058241a33b you will see some discussion on this. To quote from Tom Minka:
"You should be using K-means to find the basis, not a Gaussian mixture model. Fitting a Gaussian mixture model is not a good method for finding a GP basis, for the same reason that GMMs are not good for vector quantization. To take an extreme
example, suppose the data are generated by a single Gaussian. Then a correctly-fitted GMM will model this data with a single Gaussian, making all means the same or deleting most of the components. This is a correct model, but a poor GP basis. For
a GP basis, you want K representative and separated points from the dataset. K-means is designed to do this."
K-means is a very trivial algorithm to implement directly in C# and there is no support for it in Infer.NET.
John
