Paper Info
Title
Signal Extensions in Independent Component Analysis and Its Application for Real-Time Processing
Abstract
In this paper, we investigate some issues related to real-time processing for independent component analysis (ICA), based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). Real-time ICA processing is especially necessary for an application in dynamic mixing environment, since a batch type of ICA processing can work well only in a static or stationary mixing environment. Although there are many choices for an ICA object function to which SPSA can be applied, in this paper, we choose a diagonality of the non-linear correlation matrix as our object function. Theories and implementations of the algorithm are described. Results of computer simulation are also presented to demonstrate the effectiveness.
Year
DOI
Venue
2004
10.1109/CIT.2004.1357299
CIT
Keywords
Field
DocType
gradient learning,real-time processing,ica processing,independent component analysis,object function,ica object function,real-time ica processing,batch type,non-linear correlation matrix,signal extensions,computer simulation,objective function,learning artificial intelligence,real time processing,stochastic processes,real time,real time systems,correlation matrix
Simultaneous perturbation stochastic approximation,Matrix (mathematics),Matrix algebra,Computer science,Stochastic process,Real-time computing,Implementation,Object function,Independent component analysis,Nonlinear correlation
Conference
ISBN
Citations 
PageRank 
0-7695-2216-5
0
0.34
References 
Authors
6
4
Name
Order
Citations
PageRank
Shuxue Ding123533.84
Jie Huang2334.47
Daming Wei321544.97
Sadao Omata4173.20