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}
}
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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