JMLR
2010
13 years 6 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...
CORR
2010
Springer
13 years 10 months ago
2010
Springer
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
UAI
2008
14 years 26 days ago
2008
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
SDM
2009
SIAM
14 years 8 months ago
2009
SIAM
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...