Talks and Poster Presentations (with Proceedings-Entry):
A. Jung, S. Schmutzhard, F. Hlawatsch, A. Hero:
"Performance bounds for sparse parametric covariance estimation in Gaussian models";
Poster: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP),
Prag, Tschechische Republik;
05-22-2011
- 05-27-2011; in: "Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)",
IEEE,
(2011),
4156
- 4159.
English abstract:
We consider estimation of a sparse parameter vector that determines
the covariance matrix of a Gaussian random vector via a sparse expansion
into known "basis matrices." Using the theory of reproducing
kernel Hilbert spaces, we derive lower bounds on the variance
of estimators with a given mean function. This includes unbiased
estimation as a special case. We also present a numerical comparison
of our lower bounds with the variance of two standard estimators
(hard-thresholding estimator and maximum likelihood estimator).
German abstract:
We consider estimation of a sparse parameter vector that determines
the covariance matrix of a Gaussian random vector via a sparse expansion
into known "basis matrices." Using the theory of reproducing
kernel Hilbert spaces, we derive lower bounds on the variance
of estimators with a given mean function. This includes unbiased
estimation as a special case. We also present a numerical comparison
of our lower bounds with the variance of two standard estimators
(hard-thresholding estimator and maximum likelihood estimator).
Keywords:
Sparsity, sparse covariance estimation, variance bound, reproducing kernel Hilbert space, RKHS.
Electronic version of the publication:
http://publik.tuwien.ac.at/files/PubDat_198278.pdf
Created from the Publication Database of the Vienna University of Technology.