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Approximate least squares parameter estimation with structured observations
2014
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.
doi:10.1109/icassp.2014.6854689
dblp:conf/icassp/YellepeddiP14
fatcat:shdkhygovfbytcjtkutk6twaqm