DocumentCode
2155284
Title
Regularized split gradient method for nonnegative matrix factorization
Author
Lantéri, Henri ; Theys, Céline ; Richard, Cédric ; Mary, David
Author_Institution
Lab. Fizeau, Univ. de Nice Sophia-Antipolis, Nice, France
fYear
2011
fDate
22-27 May 2011
Firstpage
1133
Lastpage
1136
Abstract
This article deals with a regularized version of the split gradient method (SGM), leading to multiplicative algorithms. The proposed algorithm is available for the optimization of any divergence depending on two data fields under positivity constraint. The SGM-based algorithm is derived to solve the nonnegative matrix factorization (NMF) problem. An example with a Frobenius norm on both the data consistency and the penalty term is developed and applied to hyperspectral data unmixing.
Keywords
gradient methods; matrix decomposition; Frobenius norm; SGM-based algorithm; hyperspectral data unmixing; multiplicative algorithms; nonnegative matrix factorization; positivity constraint; regularized split gradient method; Convolution; Equations; Gradient methods; Hyperspectral imaging; Mathematical model; Matrix decomposition; Minimization; NMF; SGM; regularization;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location
Prague
ISSN
1520-6149
Print_ISBN
978-1-4577-0538-0
Electronic_ISBN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2011.5946608
Filename
5946608
Link To Document