DocumentCode
284750
Title
Adaptive distributed orthogonalization processing for principal components analysis
Author
Chen, Hong ; Liu, Ruey-wen
Author_Institution
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Volume
2
fYear
1992
fDate
23-26 Mar 1992
Firstpage
293
Abstract
Adaptive extraction of principal components of a vector stochastic process is a topic currently receiving much attention. The authors propose a learning algorithm implemented on a neural-like network. This algorithm is shown to be superior to previous ones. The convergence of this algorithm can be proved, but only an outline of the proof is presented
Keywords
convergence; learning (artificial intelligence); neural nets; stochastic processes; adaptive distributed orthogonalisation processing; convergence; learning algorithm; neural-like network; vector stochastic process; Adaptive signal processing; Autocorrelation; Convergence; Data analysis; Data mining; Intelligent networks; Principal component analysis; Signal processing algorithms; Statistics; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location
San Francisco, CA
ISSN
1520-6149
Print_ISBN
0-7803-0532-9
Type
conf
DOI
10.1109/ICASSP.1992.226062
Filename
226062
Link To Document