On pattern classification with Sammon's nonlinear mapping - An experimental study

Pattern Recognition

Volumn 31, Issue 4, 1998, Pages 371-381

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

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b ASTON UNIVERSITY   (United Kingdom)

Author keywords

Chromosomes; Classification; Feature extraction; Multilayer perceptron; Neural networks; Sammon's mapping

Indexed keywords

DATA STRUCTURES; MAPPING; NEURAL NETWORKS; PATTERN RECOGNITION; TOPOLOGY;

PRINCIPAL COMPONENT ANALYSIS; SAMMON NONLINEAR MAPPING;

FEATURE EXTRACTION;

EID: 0032044986     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(97)00064-2     Document Type: Article

References (18)

1. Y. Chien, Interactive Pattern Recognition. Marcel Dekker Inc., New York (1978).
2. P. A. Devijver and J. Kittler, Pattern Recognition - A Statistical Approach. Prentice-Hall, Englewood Cliffs, New Jersey (1982).
3. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990).
4. J. W. Sammon Jr., A nonlinear mapping for data structure analysis, IEEE Trans. Comput. 18, 401-409 (1969).
5. H. Bourland and Y. Kamp, Auto-association by multi-layer perceptrons and singular value decomposition, Biol. Cybernet. 59, 291-294 (1988).
6. T. Kohonen, The self organizing map, Proc. IEEE 78, 1464-1480 (1990).
7. J. Mao and A. K. Jain, Artificial neural networks for feature extraction and multivariate data projection, IEEE Trans. Neural Networks 6, 296-317 (1995).
8. J. Rubner and K. Schulten, Development of feature detectors by self-organization, Biol. Cybernet. 62, 193-199 (1990).
9. T. D. Sanger, Optimal unsupervised learning in a single-layer linear feedforward neural network, Neural Networks 2, 459-473 (1989).
10. E. Saund, Dimensionality-reduction using connectionist networks, Trans. Pattern Analysis Mach. Intell. 11, 304-314 (1989).
11. B. Lerner, H. Guterman, M. Aladjem and I. Dinstein, Unsupervised feature extraction for nonlinear supervised classification with application to chromosome analysis, Proc. Int. Conf. Neural Information Process (ICONIP'95) Beijing, vol. 1, pp 279-284 (1995).
12. I. White, Comment on 'A nonlinear mapping for data structure analysis', IEEE Trans. Comput. 21, 220-221 (1972).
13. R. D. Dony and S. Haykin, Neural network approaches to image compression, Proc. IEEE 83, 288-303 (1995).
14. G. W. Cottrell, P. Munro and D. Zipser, Learning internal representations from gray-scale images: an example of extensional programming, 9th Ann. Conf. Cognitive Sci. Soc., pp 462-473. Seattle, Erlbaum, Hilisdale (1987).
15. R. P. Gorman and T. J. Sejnowski, Analysis of hidden units in a layered network trained to classify sonar targets, Neural Networks 1, 75-89 (1988).
16. B. Lerner, H. Guterman, I. Dinstein and Y. Romem, Medial axis transform based features and a neural network for human chromosome classification, Pattern Recognition 28, 1673-1683 (1995).
17. B. Lerner, H. Guterman, I. Dinstein and Y. Romem, Human chromosome classification using multilayer perceptron neural network, Int. J. Neural Systems 6, 359-370 (1995).
18. A. Carothers and J. Piper, Computer-aided classification of human chromosomes: a review, Statist. Comput. 4, 161-171 (1994).