The Spatial-EM is a new robust EM algorithm for the finite mixture learning procedures. The algorithm utilizes median- based location and rank-based scatter estimators to replace sample mean and sample covariance matrix in each M step, hence enhancing stability and robustness of the algorithm. To understand more about this algorithm, read the article ”Yu, K., Dang, X., Bart Jr, H. and Chen, Y. (2015). Robust Model- based Learning via Spatial-EM Algorithm. IEEE Transactions on Knowledge and Data Engineering, 27(6), 1670-1682. doi:10.1109/TKDE.2014.2373355”.
| Version: | 1.0 |
| Depends: | R (≥ 2.15.0) |
| Imports: | mvtnorm, e1071, ggplot2, ellipse, doParallel, grid, foreach |
| Published: | 2015-06-05 |
| Author: | Aishat Aloba, Kai Yu, Xin Dang, Yixin Chen, and Henry Bart Jr. |
| Maintainer: | Aishat Aloba <adetokaloba at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | RobustEM results |
| Reference manual: | RobustEM.pdf |
| Package source: | RobustEM_1.0.tar.gz |
| Windows binaries: | r-devel: RobustEM_1.0.zip, r-release: RobustEM_1.0.zip, r-oldrel: RobustEM_1.0.zip |
| OS X Mavericks binaries: | r-release: RobustEM_1.0.tgz, r-oldrel: RobustEM_1.0.tgz |
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