On Compressive Sensing of Sparse Covariance Matrices Using Deterministic Sensing Matrices

Alihan Kaplan; Volker Pohl; Dae Gwan Lee

ICASSP 2018accepted0 citations

On Compressive Sensing of Sparse Covariance Matrices Using Deterministic Sensing Matrices

Alihan Kaplan, Volker Pohl, Dae Gwan Lee

Abstract

This paper considers the problem of determining the sparse covariance matrix X of an unknown data vector x by observing the covariance matrix Y of a compressive measurement vector y = Ax. We construct deterministic sensing matrices A for which the recovery of a k-sparse covariance matrix X from m values of Y is guaranteed with high probability. In particular, we show that the number of measurements m scales linearly with the sparsity k.

BibTeX

@inproceedings{icassp2018_oncompressivesen,
  title = {On Compressive Sensing of Sparse Covariance Matrices Using Deterministic Sensing Matrices},
  author = {Alihan Kaplan and Volker Pohl and Dae Gwan Lee},
  booktitle = {ICASSP 2018},
  year = {2018}
}