Graph Theoretic Techniques for Pruning Data and Their Applications

IEEE Transactions on Signal Processing

Volumn 46, Issue 9, 1998, Pages 2574-2579

  Hoya, Tetsuya ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Data pruning algorithms; Graph theoretic techniques; Pattern classification

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; GRAPH THEORY; MULTILAYER NEURAL NETWORKS; VECTOR QUANTIZATION;

DATA PRUNING;

PATTERN RECOGNITION;

EID: 0032165337     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.709550     Document Type: Article

References (20)

1. J. Moody and C. J. Darken, "Fast learning in networks of locally-tuned processing units," Neural Networks, vol. 6, no. 4, pp. 525-533, 1989.
2. P. A. Devijer and J. Kittler, Pattern Recognition: A Statistical Approach. Englewood Cliffs, NJ: Prentice-Hall, 1982.
3. M. A. Kraaijveld and R. P. W. Duin, "Generalization capabilities of minimal kernel-based networks," in Proc. Int. Joint Conf. Neural Networks, Seattle, WA, 1991.
4. J. G. Augustson and J. Minker, "An analysis of some graph theoretical cluster techniques," J. ACM, vol. 17, no. 4, pp. 571-588, Oct. 1970.
5. D. W. Matula, Classification and Clustering: Graph Theoretic Techniques/or Cluster Analysis Algorithms, J. Van Ryzin, Ed. New York: Academic, 1977, pp. 95-129.
6. C. T. Zahn, "Graph-theoretic methods for detecting and describing gestalt clusters," IEEE Trans. Comput., vol. C20, pp. 68-86, 1971.
7. H. Tahani, B. Plummer, and N. S. Hemamalini, "A new data reduction algorithm for pattern classification," in Proc. ICASSP, 1996, pp. ," in 3446-3449.
8. J. B. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proc. Symp. Math. Stat. Prob., 5th ed. Berkeley, CA: Univ. of Calif. Press, 1967, vol. 1, pp. 281-297.
9. T. Kohonen, "Improved versions of learning vector quantization," in Proc. Int. Joint Conf. Neural Networks, San Diego, CA, 1990, vol. 1, pp. 545-550.
10. N. Christofides, Graph Theory: An Algorithmic Approach. New York: Academic, 1975.
11. O. J. Morris, M. de J. Lee, and A. G. Constantinides, "Graph theory for image analysis: An approach based on the shortest spanning tree," Proc. Inst. Elect. Eng., pt. F, vol. 133, no. 2, Apr. 1986.
12. M. Huckvale, "Speech filing system Vs3.0-Computer tools for speech research," Tech. Rep., Univ. Coll. London, London, U.K., Mar. 1996.
13. T. Nakajima, T. Suzuki, H. Ohmura, S. Ishizaki, and K. Tanaka, "Estimation of vocal tract area function by adaptive deconvolution and adaptive speech analysis system," J. Acoust. Soc. Jpn.
14. T. Hoya, H. Ihara, T. Asano, and Y. Ishida, "Word recognition by neural networks using adaptive mel-cepstral analysis," in Proc. Int. Workshop Intell. Signal Process. Commun. Syst., Seoul, Korea, 1994, pp. 68-71.
15. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA: MIT Press, 1986, vol. 34, no. 3, p. 157 (in Japanese).
16. S. Haykin, Neural Networks: A Comprehensive Foundation. New York: Macmillan, 1994.
17. K. Nakano et al. Introduction and Practice: Neuro Computer. Tokyo, Japan: Gijutsu Hyoron, 1989 (in Japanese).
18. D. F. Specht, "A general regression neural network," IEEE Trans. Neural Networks, vol. 2, pp. 568-576, 1991.
19. P. D. Wasserman, "Advanced methods in neural computing," in Radial Basis-Function Networks. New York: Van Nostrand Reinhold, 1993, pp. 147-176, ch. 8.
20. T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, and K. Torkkola, "LVQ-PAK: The learning vector quantization program package-Version 3.1," Apr. 1995.