Paper Info
Title
On The Sensitivity Of Spectral Initialization For Noisy Phase Retrieval
Abstract
The spectral method is an important approach for signal estimation that is often used as an initialization to iterative methods as well as a stand-alone estimator, where the signal is estimated by the top eigenvector of certain carefully-constructed data matrix. A recent line of work has characterized the asymptotic behavior of such data matrices used in spectral methods, which reveals an interesting phase transition phenomenon: there exists a critical sampling threshold below which the estimate of the spectral method is uninformative. Furthermore, optimal preprocessing functions are developed to minimize this critical sampling threshold. In particular, most of the existing work is focused on the noiseless phase retrieval problem. In this paper, our goal is to examine the sensitivity of such optimal preprocessing functions in noisy phase retrieval, when there is a mismatch between the noise model used in deriving the optimal preprocessing function and the actual noise model in practice. Our results provide important insights into the choice of preprocessing functions in spectral methods.
Year
DOI
Venue
2019
10.1109/icassp.2019.8682584
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
spectral method, phase retrieval, noise sensitivity
Phase retrieval,Noise measurement,Pattern recognition,Iterative method,Computer science,Spectral method,Artificial intelligence,Initialization,Additive white Gaussian noise,Eigenvalues and eigenvectors,Estimator
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Vincent Monardo101.01
Yuejie Chi272056.67