Disentangling Gaussians.
The article discusses the Gaussian mixture model (GMM), a statistical moodel comprised of heterogeneous Gaussian sources, and presents an algorithm which is able to recover the parameters of Gaussians which has polynomial sample complexity and computational complexity. The article discusses one-dime...
| Publicado en: | Communications of the ACM Vol. 55; no. 2; pp. 113 - 121 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Association for Computing Machinery
Feb2012
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | The article discusses the Gaussian mixture model (GMM), a statistical moodel comprised of heterogeneous Gaussian sources, and presents an algorithm which is able to recover the parameters of Gaussians which has polynomial sample complexity and computational complexity. The article discusses one-dimensional GMM and discusses issues related to clustering, overlapping Gaussians, and decomposition. The authors argue that their algorithm can provide a basis for developing estimators or other algorithms with practical utilities. |
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