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A Spectral Estimation Framework for Phase Retrieval via Bregman Divergence Minimization
[article]
2020
arXiv
pre-print
In this paper, we develop a novel framework to optimally design spectral estimators for phase retrieval given measurements realized from an arbitrary model. We begin by deconstructing spectral methods, and identify the fundamental mechanisms that inherently promote the accuracy of estimates. We then propose a general formalism for spectral estimation as approximate Bregman loss minimization in the range of the lifted forward model that is tractable by a search over rank-1, PSD matrices.
arXiv:2012.01652v2
fatcat:45gxt4zvqrcz5oprjowr7j3bu4