Low-rank matrix recovery from non-linear observations

Algorithms for sparse recovery problems from non-linear measurements have attracted some attention lately. Closely related to the problem of sparse is recovery is the problem of low-rank matrix recovery. There is no work on the topic of low-rank matrix recovery from non-linear measurements. This is...

Full description

Saved in:
Bibliographic Details
Main Authors: Bhattacharjee, Protim, Khurana, Prerna, Majumdar, Angshul
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Conferences
Digital signal processing
Inverse problems
Ky-Fan norm
Magnetic resonance imaging
matrix completion
Matrix decomposition
Minimization
non-linear inverse problem
Nonlinearity
Norms
nuclear norm
Optimization
Protons
Recovery
Signal processing algorithms
Sparse matrices
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Algorithms for sparse recovery problems from non-linear measurements have attracted some attention lately. Closely related to the problem of sparse is recovery is the problem of low-rank matrix recovery. There is no work on the topic of low-rank matrix recovery from non-linear measurements. This is the first study that proposes two algorithms for the said problem. The first one is based on nuclear norm minimization while the second one is based on Ky-Fan norm minimization.
ISSN:1546-1874
2165-3577
DOI:10.1109/ICDSP.2015.7251949