Paper Info
Title
An effective decoupling method for matrix optimization and its application to the ICA problem
Abstract
Matrix optimization of cost functions is a common problem. Construction of methods that enable each row or column to be individually optimized, i.e., decoupled, are desirable for a number of reasons. With proper decoupling, the convergence characteristics such as local stability can be improved. Decoupling can enable density matching in applications such as independent component analysis (ICA). Lastly, efficient Newton algorithms become tractable after decoupling. The most common method for decoupling rows is to reduce the optimization space to orthogonal matrices. Such restrictions can degrade performance. We present a decoupling procedure that uses standard vector optimization procedures while still admitting nonorthogonal solutions. We utilize the decoupling procedure to develop a new decoupled ICA algorithm that uses Newton optimization enabling superior performance when the sample size is limited.
Year
DOI
Venue
2012
10.1109/ICASSP.2012.6288271
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
Newton method,convergence of numerical methods,independent component analysis,matrix algebra,ICA problem,Newton algorithms,convergence characteristic,cost function,decoupling procedure,density matching,effective decoupling method,independent component analysis,local stability,matrix optimization,standard vector optimization,Independent component analysis (ICA),blind source separation (BSS),matrix optimization
Approximation algorithm,Orthogonal matrix,Mathematical optimization,Matrix (mathematics),Vector optimization,Computer science,Matrix decomposition,Decoupling (cosmology),Independent component analysis,Newton's method
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4673-0044-5
978-1-4673-0044-5
13
PageRank 
References 
Authors
0.63
6
4
Name
Order
Citations
PageRank
Matthew Anderson126314.64
Xi-Lin Li254734.85
Pedro A Rodriguez3433.44
Tülay Adali41690126.40