  Sparse Inverse Covariance Estimation (SICE) is useful in many practical data
analyses. Recovering the connectivity, non-connectivity graph of covariates is
classified amongst the most important data mining and learning problems. In
this paper, we introduce a novel SICE approach using adaptive thresholding. Our
method is based on updates in a transformed domain of the desired matrix and
exponentially decaying adaptive thresholding in the main domain (Inverse
Covariance matrix domain). In addition to the proposed algorithm, the
convergence analysis is also provided. In the Numerical Experiments Section, we
show that the proposed method outperforms state-of-the-art methods in terms of
accuracy.
