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

@inproceedings{Cao2008CovarianceEF,
  title={Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform},
  author={Guangzhi Cao and Charles A. Bouman},
  booktitle={NIPS},
  year={2008}
}
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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References

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Covariance estimation for high dimensional data vectors using the sparse matrix transform

G. Cao, C. A. Bouman
Purdue University, Technical Report ECE 08-05, 2008. • 2008
View 6 Excerpts
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Fast Reconstruction Algorithms for Optical Tomography Using Sparse Matrix Representations

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro • 2007
View 2 Excerpts

On information and sufficiency

R. A. Leibler
J . Multivar . Anal . • 2004

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