matrix_and_statistical_ai_reading_group
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| Mailing list: matrix-sai at utlists.utexas | Mailing list: matrix-sai at utlists.utexas | ||
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| + | === III === | ||
| + | |Date | 31 Mar 2010. | | ||
| + | |Paper | Pradeep Ravikumar et al: High-dimensional covariance estimation by minimizing l_1-penalized log-determinant divergence http://www.stat.berkeley.edu/tech-reports/767.pdf| | | ||
| + | |Presenter | Matyas Sustik | | | ||
| + | |Notes | This paper contains a good introduction to covariance matrix estimation; the "large p small n case"; the connection to logdet divergence before delving into its main results. | | | ||
| === II. Intro to the use of Support graphs in finding preconditioners === | === II. Intro to the use of Support graphs in finding preconditioners === | ||
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| ===== Papers proposed ===== | ===== Papers proposed ===== | ||
| - | |Pradeep Ravikumar et al: High-dimensional covariance estimation by minimizing l_1-penalized log-determinant divergence http://www.stat.berkeley.edu/tech-reports/767.pdf| | | ||
| - | |Matyas Sustik | | | ||
| - | |This paper contains a good introduction to covariance matrix estimation; the "large p small n case"; the connection to logdet divergence before delving into its main results. | | | ||
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