"Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7195 |
| Date | 21 April 1995 |
| Creators | Jordan, Michael, Xu, Lei |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 9 p., 291671 bytes, 476864 bytes, application/postscript, application/pdf |
| Relation | AIM-1520, CBCL-111 |
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