Low Rank Phase Retrieval

Abstract

We develop two iterative algorithms for solving the low rank phase retrieval (LRPR) problem. LRPR refers to recovering a low-rank matrix $\X$ from magnitude-only (phaseless) measurements of random linear projections of its columns. Both methods consist of a spectral initialization step followed by an iterative algorithm to maximize the observed data likelihood. We obtain sample complexity bounds for our proposed initialization approach to provide a good approximation of the true $\X$. When the rank is low enough, these bounds are significantly lower than what existing single vector phase retrieval algorithms need. Via extensive experiments, we show that the same is also true for the proposed complete algorithms.

To appear in IEEE Trans. Signal Processing, 2017

References in corpus (4)

• Fast matrix completion without the condition number
• Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow
• Solving Large-scale Systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow
• Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations

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• Iterative X-ray Spectroscopic Ptychography
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• Non-Convex Structured Phase Retrieval
• Spectral Algorithm for Low-rank Multitask Regression
• A Simple Generalization of a Result for Random Matrices with Independent Sub-Gaussian Rows
• Provable Low Rank Phase Retrieval