Adaptive distributed orthogonalization processing for principal components analysis
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2 (15206149) , 293-296 vol.2
- https://doi.org/10.1109/icassp.1992.226062
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
This publication has 11 references indexed in Scilit:
- An alternative proof of convergence for Kung-Diamantaras APEX algorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A neural network learning algorithm for adaptive principal component extraction (APEX)Published by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Constrained principal component analysis via an orthogonal learning networkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Efficient, numerically stabilized rank-one eigenstructure updating (signal processing)IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Orthogonal learning network for constrained principal component problemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Neural networks and principal component analysis: Learning from examples without local minimaNeural Networks, 1989
- Adaptive network for optimal linear feature extractionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Simple, effective computation of principal eigenvectors and their eigenvalues and application to high-resolution estimation of frequenciesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrixJournal of Mathematical Analysis and Applications, 1985
- Simplified neuron model as a principal component analyzerJournal of Mathematical Biology, 1982