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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7195| Title: | On Convergence Properties of the EM Algorithm for Gaussian Mixtures |
| Keywords: | learning neural networks EM algorithm clustering mixture models statistics |
| Issue Date: | 9-Oct-2013 |
| Description: | "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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1520 CBCL-111 http://hdl.handle.net/1721.1/7195 |
| Appears in Collections: | MIT Items |
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