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Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference
2018
IEEE Transactions on Pattern Analysis and Machine Intelligence
We propose an algorithm for simplifying a finite mixture model into a reduced mixture model with fewer mixture components. The reduced model is obtained by maximizing a variational lower bound of the expected log-likelihood of a set of virtual samples. We develop three applications for our mixture simplification algorithm: recursive Bayesian filtering using Gaussian mixture model posteriors, KDE mixture reduction, and belief propagation without sampling. For recursive Bayesian filtering, we
doi:10.1109/tpami.2018.2845371
pmid:29994194
fatcat:5qm3jc4j7fesrg7j4wjixvtqne