A comparative study of neural network based feature extraction paradigms

Pattern Recognition Letters

Volumn 20, Issue 1, 1999, Pages 7-14

  Lerner, Boaz ^a   Guterman, Hugo ^b   Aladjem, Mayer ^b   Dinstein, Its'hak ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Auto associative neural network; Classification; Data projection; Feature extraction; Multilayer perceptron; Principal components; Sammon's mapping

Indexed keywords

ASSOCIATIVE PROCESSING; DATA STRUCTURES; DATABASE SYSTEMS; IMAGE PROCESSING; MULTILAYER NEURAL NETWORKS; STATISTICAL METHODS;

AUTO-ASSOCIATIVE NEURAL NETWORK; PRINCIPAL COMPONENT ANALYSIS;

FEATURE EXTRACTION;

ARTIFICIAL NEURAL NETWORK;

EID: 0032822143     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(98)00120-2     Document Type: Article

References (13)

1. Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford University Press, Oxford.
2. Bourland H., Kamp Y. Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59:1988;291-294.
3. Cottrell, G.W., Munro, P., Zipser, D., 1987. Learning internal representations from gray-scale images: An example of extensional programming. In: 9th Annual Conference for Cognitive Science Society, Hillsdale, Erlbaum, Seattle, pp. 462-473.
4. Fukunaga, K., 1990. Introduction to Statistical Pattern Recognition. 2nd ed. Academic Press, New York.
5. Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal. 37:1991;233-243.
6. Lerner, B., 1998. Toward a completely automatic neural network based human chromosome analysis. IEEE Trans. Syst. Man Cyber. 28, Part B, Special Issue on Artificial Neural Networks, pp. 544-552.
7. Lerner B., Guterman H., Dinstein I., Romem Y. Medial axis transform based features and a neural network for human chromosome classification. Pattern Recognition. 28:1995;1673-1683.
8. Lerner B., Guterman H., Aladjem M., Dinstein I., Romem Y. On pattern classification with Sammon's nonlinear mapping - an experimental study. Pattern Recognition. 31:1998;371-381.
9. Lowe D., Tipping M.E. Feed-forward neural networks and topographic mappings for exploratory data analysis. Neural Computing and Applications. 4:1996;83-95.
10. Mao J., Jain A.K. Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Networks. 6:1995;296-317.
11. Merz, C.J., Murphy, P.M., 1998. UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA.
12. Michie, D., Spiegelhalter D.J., Taylor, C.C., 1994. Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York.
13. Sammon, J.W., Jr., 1969. A nonlinear mapping for data structure analysis. IEEE Trans. Computers 18, 401-409.