Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform

  title={Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform},
  author={Guangzhi Cao and Charles A. Bouman},
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 (ML) approach to covariance estimation, which employs a novel 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 Givens rotations. Using… CONTINUE READING
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Covariance estimation for high dimensional data vectors using the sparse matrix transform

G. Cao, C. A. Bouman
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