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...
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 ...
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...
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...