41 search results - page 1 / 9 » Shrinkage estimation of high dimensional covariance matrices |
ICASSP
2009
IEEE
13 years 11 months ago
2009
IEEE
We address covariance estimation under mean-squared loss in the Gaussian setting. Specifically, we consider shrinkage methods which are suitable for high dimensional problems wit...
NIPS
2008
13 years 6 months ago
2008
Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
TSP
2010
12 years 11 months ago
2010
We address covariance estimation in the sense of minimum mean-squared error (MMSE) when the samples are Gaussian distributed. Specifically, we consider shrinkage methods which are ...
JMLR
2010
12 years 11 months ago
2010
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
KDD
2004
ACM
14 years 4 months ago
2004
ACM
The computation of covariance and correlation matrices are critical to many data mining applications and processes. Unfortunately the classical covariance and correlation matrices...