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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.226062
