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Sparse Inverse Covariance Estimation (SICE) is useful in many practical dataanalyses. Recovering the connectivity, non-connectivity graph of covariates isclassified amongst the most important data mining and learning problems. Inthis paper, we introduce a novel SICE approach using adaptive thresholding. Ourmethod is based on updates in a transformed domain of the desired matrix andexponentially decaying adaptive thresholding in the main domain (InverseCovariance matrix domain). In addition to the proposed algorithm, theconvergence analysis is also provided. In the Numerical Experiments Section, weshow that the proposed method outperforms state-of-the-art methods in terms ofaccuracy.

A Novel Approach to Sparse Inverse Covariance Estimation Using Transform Domain Updates and Exponentially Adaptive Thresholding