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...
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...
We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
Background: Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically all...
Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated ...