A spectral method for stable bispectrum inversion with application to multireference alignment
Chen, Hua
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/100064
Description
Title
A spectral method for stable bispectrum inversion with application to multireference alignment
Author(s)
Chen, Hua
Contributor(s)
Zhao, Zhizhen
Issue Date
2018-05
Keyword(s)
Multireference Alignment
Bispectrum
Abstract
This thesis aims to develop an alignment-free method to estimate the underlying
signal from a large number of noisy randomly shifted observations,
called multi-reference alignment problem. Specifically, we make use of invariant
features including mean, power spectrum, and the bispectrum of the
signal from the observations. We propose a new algorithm using spectral
decomposition of the bispectrum phase matrix for this specific problem. For
clean signals, we show that the eigenvectors of the bispectrum phase matrix
correspond to the true phases of the signal and its shifted copies. In noisy
cases, we will select one eigenvector with largest spectral gap to estimate
the original signal. Such spectral method is robust to noise and empirically
comparable to iterative phase synchronization and optimization on phase
manifold for noise variance sigma squared less than or equal to 0.32. It can be also be used as a stable and
efficient initialization technique for local non-convex optimization for bispectrum
inversion. Using 12-fold symmetric property of bispectrum, we are able
to increase our computational efficiency by roughly ten times.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.