The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning mixtures of arbitrary gaussians
Abstract
Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a local-search heuristic from Dempster, Laird and Rubin~(1977). However, such heuristics are known to require time exponential in the dimension (i.e., number of variables) in the worst case, even when the number of components is $2$.
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Cites
Huber, P. J. (1985) · The Annals of Statistics · doi:10.1214/aos/1176349519 · Cited by 1,277
Cited by
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Achlioptas, D. (2001) · Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/375551.375608 · Cited by 699
Metadata
- Authors
- Sanjeev, A., & Kannan, R.
- Venue
- Proceedings of the thirty-third annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2001-07-06
- Pages
- 247-257
- OpenAlex
- W1969015668
- Cited by
- 216
- References
- 1
- FWCI
- 1.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sanjeev, A., & Kannan, R. (2001). Learning mixtures of arbitrary gaussians. Proceedings of the thirty-third annual ACM symposium on Theory of computing. https://doi.org/10.1145/380752.380808
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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