Low rank phase retrieval

Nayer, Seyedehsara; Vaswani, Namrata; Eldar, Yonina C.

doi:10.1109/icassp.2017.7952997

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

2024

Publication Types

Select...

Article2

Other2

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An early version of the initialization idea developed in this work was presented at the IEEE Statistical Signal Processing Workshop 2016 [1]. A short version of this work will be presented at ICASSP 2017 [2]. the old Gerchberg-Saxton algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Phase Retrieval

Vaswani

Nayer

Eldar

2017

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%