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# The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals

@article{Cao2011TheSM, title={The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals}, author={Guangzhi Cao and Leonardo R. Bachega and Charles A. Bouman}, journal={IEEE Transactions on Image Processing}, year={2011}, volume={20}, pages={625-640} }

- Published 2011 in IEEE Transactions on Image Processing
DOI:10.1109/TIP.2010.2071390

Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition which can be represented as a sparse matrix transform (SMT). The SMT is formed by a product of pairwise coordinate rotations known as… CONTINUE READING

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