This toolbox implements variational inference for Gaussian mixture models (vbGMM) as per Chapter 10 of Pattern Recognition and Machine Learning by C. M. Bishop (2006). Part of the code is based on a barebone MATLAB implementation by Mo Chen. vbGMM contains a number of additional features:
- Speed up initialization with fast K-means algorithm (Charles Elkan, "Using the Triangle Inequality to Accelerate k-Means", 2003; link)
- Generate samples from the trained mixture model (
vbgmmrnd.m) - Expected pdf of the trained vbGMM at any given point (
vbgmmpdf.m) - Speed up inference by killing near-empty components (to be perfected)
- Support for bounded variables (data are transformed for inference to an unbounded space via a nonlinear transformation) -- to be implemented.
- Generate marginal and conditional vbGMMs -- to be implemented.
The toolbox is still work in progress and currently incomplete.