Approximate least squares parameter estimation with structured observations

@article{Yellepeddi2014ApproximateLS,
  title={Approximate least squares parameter estimation with structured observations},
  author={Atulya Yellepeddi and James C. Preisig},
  journal={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2014},
  pages={5671-5675}
}
  • Atulya Yellepeddi, J. Preisig
  • Published 4 May 2014
  • Mathematics, Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
The solution of inverse problems where the parameter being estimated has a known structure has been widely studied. In this work, we consider the situation where it is not appropriate to assume a structure for the parameter, but the observations on which the estimate are based are structured; specifically, when the observations are parametrized by a decomposable graphical model. This translates to structured sparsity of the inverse covariance matrix for Gaussian distributed observation vectors… 

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