Title :
A unified sequential method for PCA
Author :
Wong, A.S.Y. ; Wong, K.W. ; Leung, Chung S.
Author_Institution :
City Univ. of Hong Kong, Kowloon, Hong Kong
Abstract :
We propose a strictly local unified sequential method for principal component analysis. Any principal component analysis algorithm for linear feedforward neural networks can be used as the weight updating equation in our method. When principal components are extracted one by one sequentially, we suggest that the initial weight vector for the next component extraction should be orthogonal to the eigen-subspace already extracted. Simulation results show that both the convergence and the precision of the extraction are improved. Our method is also capable of extracting full eigenspace by using the neural network approach
Keywords :
eigenvalues and eigenfunctions; feature extraction; feedforward neural nets; learning (artificial intelligence); principal component analysis; sequential estimation; PCA; PCA algorithm; convergence; eigen-subspace; feature extraction; full eigenspace extraction; initial weight vector; learning algorithm; linear feedforward neural networks; next component extraction; principal component analysis; simulation results; strictly local unified sequential method; unified sequential method; weight updating equation; Convergence; Equations; Feature extraction; Feedforward neural networks; Gaussian processes; Neural networks; Neurons; Principal component analysis; Vectors;
Conference_Titel :
Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on
Conference_Location :
Pafos
Print_ISBN :
0-7803-5682-9
DOI :
10.1109/ICECS.1999.812352
