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Fusion frames and robust dimension reduction
2008
2008 42nd Annual Conference on Information Sciences and Systems
We consider the linear minimum meansquared error (LMMSE) estimation of a random vector of interest from its fusion frame measurements in presence noise and subspace erasures. Each fusion frame measurement is a low-dimensional vector whose elements are inner products of an orthogonal basis for a fusion frame subspace and the random vector of interest. We derive bounds on the mean-squared error (MSE) and show that the MSE will achieve its lower bound if the fusion frame is tight. We prove that
doi:10.1109/ciss.2008.4558533
dblp:conf/ciss/PezeshkiKC08
fatcat:odbuibycejhqdlk6xkupha6xhu