Paper Info
Title
Nonlinear Independent Component Analysis by Self-Organizing Maps
Abstract
Linear Independent Component Analysis considers the problem of finding a linear transformation that makes the components of the output vector statistically independent. This can be applied to blind source separation, where the input data consist of unknown linear mixtures of unknown independent source signals. The original source signals can be recovered from their mixtures using the assumption that they are statistically independent. More generally we can consider nonlinear mappings that make...
Year
DOI
Venue
1996
10.1007/3-540-61510-5_137
ICANN
Keywords
Field
DocType
nonlinear independent component analysis,self-organizing maps,linear transformation,statistical independence,blind source separation,linear independence,independent component analysis,data consistency
Linear independence,Nonlinear system,Pattern recognition,Self-organizing map,Independent component analysis,Artificial intelligence,Linear map,Component analysis,Blind signal separation,Independence (probability theory),Mathematics
Conference
ISBN
Citations 
PageRank 
3-540-61510-5
6
0.61
References 
Authors
10
1
Name
Order
Citations
PageRank
P Pajunen129244.65