DocumentCode :
3573274
Title :
An accurate and fast neural method for PCA extraction
Author :
Filho, J. B O Souza ; Cal?´ba, L.P. ; Seixas, J.M.
Author_Institution :
Signal Process. Lab., Fed. Univ. of Rio de Janeiro, Brazil
Volume :
1
fYear :
2003
Firstpage :
797
Abstract :
Principal component analysis (PCA) is a characteristic extraction method, whose main objective function is the reconstruction of the original data space. PCA is a linear optimal method, in the sense of mean squared error, and is applied in a wide variety of knowledge areas. In this paper, a new neural method for PCA extraction is proposed and compared, in terms of accuracy and computational costs, to other well accepted neural extraction methods, such as GHA and APEX. The performance comparison was evaluated using preprocessed spectra from passive sonar signals. It was verified that the proposed method performed better than all other methods, exhibiting easier implementation, lower computational costs and higher accuracy.
Keywords :
Hebbian learning; feature extraction; neural nets; principal component analysis; signal processing; APEX; characteristic extraction method; computational costs; fast neural method; linear optimal method; mean square error; neural extraction methods; passive sonar signals; preprocessed spectra; principal component analysis; Computational efficiency; Data analysis; Data mining; Equations; Image reconstruction; Minimization methods; Principal component analysis; Signal processing; Sonar; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1223484
Filename :
1223484
Link To Document :
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