Loading...
Loading...

Go to the content (press return)

Matrix completion and extrapolation via kernel regression

Author
Gimenez, P.; Pagès-Zamora, A.; Giannakis, G.B.
Type of activity
Journal article
Journal
IEEE transactions on signal processing
Date of publication
2019-10-01
Volume
67
Number
19
First page
5004
Last page
5017
DOI
10.1109/TSP.2019.2932875
Project funding
COMONSENS Network
Coding and Signal Processing for Emerging Wireless Communication and Sensor Networks
Repository
http://hdl.handle.net/2117/174782 Open in new window
URL
https://ieeexplore.ieee.org/document/8786233 Open in new window
Abstract
Matrix completion and extrapolation (MCEX) are dealt with here over reproducing kernel Hilbert spaces (RKHSs) in order to account for prior information present in the available data. Aiming at a fast and low-complexity solver, the task is formulated as one of kernel ridge regression. The resultant MCEX algorithm can also afford online implementation, while the class of kernel functions also encompasses several existing approaches to MC with prior information. Numerical tests on synthetic and rea...
Citation
Gimenez, P.; Pagès-Zamora, A.; Giannakis, G.B. Matrix completion and extrapolation via kernel regression. "IEEE transactions on signal processing", 1 Octubre 2019, vol. 67, núm. 19, p. 5004-5017.
Keywords
Extrapolation, Graphs, Kernel ridge regression, Matrix completion, Online learning, RKHS
Group of research
SPCOM - Signal Processing and Communications Group

Participants

Attachments