Result: Some empirical advances in matrix completion

Title:
Some empirical advances in matrix completion
Authors:
MAJUMDAR, A, WARD, R. K
Source:
Signal processing. 91(5):1334-1338
Publisher Information:
Amsterdam: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Traitement du signal, Signal processing, Divers, Miscellaneous, Algorithme rapide, Fast algorithm, Algoritmo rápido, Calcul matriciel, Matrix calculus, Cálculo de matrices, Complétion, Completion, Compleción, Echantillonnage aléatoire, Random sampling, Muestreo aleatorio, Optimisation, Optimization, Optimización, Problème NP difficile, NP hard problem, Problema NP duro, Programmation convexe, Convex programming, Programación convexa, Programmation non convexe, Non convex programming, Programación no convexa, Traitement signal, Signal processing, Procesamiento señal, Blue-noise sampling, Matrix completion, Non-convex optimization
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T1Z4, Canada
ISSN:
0165-1684
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=23872621
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.23872621
Database:
PASCAL Archive

Further Information

Solving the matrix completion problem via rank minimization is NP hard. Recent studies have shown that this problem can be addressed as a convex nuclear-norm minimization problem, albeit at an increase in the required number of samples. This paper proposes a non-convex optimization problem (a variant of convex nuclear-norm minimization) for the solution of matrix completion. It also develops a fast numerical algorithm to solve the optimization. This empirical study shows that significant improvement can be achieved by the proposed method compared to the previous ones. The number of required samples is also dependent on the type of sampling scheme used. This work shows that blue-noise sampling schemes yield more accurate matrix completion results compared to the popular uniform random sampling.