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Analysis of Spectral Methods for Phase Retrieval With Random Orthogonal Matrices

Rishabh Dudeja,Milad Bakhshizadeh,Junjie Ma,A. Maleki

Published 2019 in IEEE Transactions on Information Theory

ABSTRACT

Phase retrieval refers to algorithmic methods for recovering a signal from its phaseless measurements. There has been recent interest in understanding the performance of local search algorithms that work directly on the non-convex formulation of the problem. Due to the non-convexity of the problem, the success of these local search algorithms depends heavily on their starting points. The most widely used initialization scheme is the spectral method, in which the leading eigenvector of a data-dependent matrix is used as a starting point. Recently, the performance of the spectral initialization was characterized accurately for measurement matrices with independent and identically distributed entries. This paper aims to obtain the same level of knowledge for isotropically random column-orthogonal matrices, which are substantially better models for practical phase retrieval systems. Towards this goal, we consider the asymptotic setting in which the number of measurements <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>, and the dimension of the signal, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, diverge to infinity with <inline-formula> <tex-math notation="LaTeX">$m/n = \delta \in (1,\infty )$ </tex-math></inline-formula>, and obtain a simple expression for the overlap between the spectral estimator and the true signal vector.

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