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
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;
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications, 2003. CIMSA '03. 2003 IEEE International Symposium on
Print_ISBN :
0-7803-7783-4
DOI :
10.1109/CIMSA.2003.1227210
