DocumentCode
1984491
Title
Neural network principal component using adaptive principal component extractor (APEX)
Author
Ali, Abed Haider
Author_Institution
Raytheon Inf. Technol. & Sci. Support, Pasadena, CA, USA
fYear
2003
fDate
29-31 July 2003
Firstpage
101
Lastpage
106
Abstract
The neural networks principal component analysis (NNPCA) can be a very useful tool in the analysis of data with very large temporal dimensionality. Considerable computer resources (computer memory and CPU-time) could be saved when processing a large data matrix. The neural network principal component analysis (NNPCA) is reviewed and an application to simulated climate data is introduced.
Keywords
climatology; covariance matrices; data analysis; geophysics computing; neural nets; principal component analysis; CPU time; adaptive principal component extractor; computer memory; computer resources; data analysis; large data matrix; neural networks principal component analysis; simulated climate data; temporal dimensionality; Biological neural networks; Covariance matrix; Data mining; Eigenvalues and eigenfunctions; Feature extraction; Matrix decomposition; Neural networks; Neurons; Principal component analysis; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence for Measurement Systems and Applications, 2003. CIMSA '03. 2003 IEEE International Symposium on
Print_ISBN
0-7803-7783-4
Type
conf
DOI
10.1109/CIMSA.2003.1227210
Filename
1227210
Link To Document