DocumentCode
1646352
Title
Comparison of Lagrange constrained neural network with traditional ICA methods
Author
Szu, Harold ; Kopriva, Ivica
Author_Institution
Digital Media RF Lab., George Washington Univ., DC, USA
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
466
Lastpage
471
Abstract
The paper presents comparison between the a priori MaxEnt and the a posteriori MaxEnt methodologies, namely Lagrange Constraint Neural Network (LCNN) by Szu in 1997 and ICA algorithms by Bell-Sejnowski-Amari-Oja (BSAO) and many others since 1996. We chose the remote sensing application because it is the only real world application that we know to be truly linear, single path, and instantaneous mixing of the unknown ground spectral objects
Keywords
constraint handling; entropy; neural nets; transfer functions; LCNN; Lagrange Constraint Neural Network; a posteriori MaxEnt; a priori MaxEnt; mixing matrices; transfer function; Covariance matrix; Data models; Entropy; Filtering; Independent component analysis; Lagrangian functions; Neural networks; Remote sensing; Stochastic processes; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1005517
Filename
1005517
Link To Document