DocumentCode :
2185758
Title :
Low-rank matrix recovery from non-linear observations
Author :
Bhattacharjee, Protim ; Khurana, Prerna ; Majumdar, Angshul
Author_Institution :
IIIT-Delhi, India
fYear :
2015
fDate :
21-24 July 2015
Firstpage :
623
Lastpage :
627
Abstract :
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.
Keywords :
Inverse problems; Magnetic resonance imaging; Matrix decomposition; Minimization; Protons; Signal processing algorithms; Sparse matrices; Ky-Fan norm; matrix completion; non-linear inverse problem; nuclear norm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing (DSP), 2015 IEEE International Conference on
Conference_Location :
Singapore, Singapore
Type :
conf
DOI :
10.1109/ICDSP.2015.7251949
Filename :
7251949
Link To Document :
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