A class of competitive learning models which avoids neuron underutilization problem

IEEE Transactions on Neural Networks

Volumn 9, Issue 6, 1998, Pages 1258-1269

  Choy, Clifford Sze Tsan ^b   Siu, Wan Chi ^a,b  

^a IEEE

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Multiplicatively biased competitive learning; Neuron underutilization problem; Vector quantization

Indexed keywords

COMPUTATIONAL COMPLEXITY; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING; VECTOR QUANTIZATION;

COMPETITIVE LEARNING MODELS; NEURON UNDERUTILIZATION PROBLEM;

LEARNING ALGORITHMS;

EID: 0032208399     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.728374     Document Type: Article

References (30)

1. T. Kohonen, Self-Organizing Maps. New York: Springer-Verlag, 1995.
2. _, "Self-organized formation of topologically correct feature maps," Biol. Cybern., vol. 43, pp. 59-69, 1982.
3. R. Duda and P. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
4. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Engl-wood Cliffs, NJ: Prentice-Hall, 1988.
5. S. Grossberg, "Adaptive pattern classification and universal recoding; II. Feedback, expectation, olfaction, illusions," Biol. Cybern., vol. 23, pp. 187-202, 1976.
6. D. E. Rumelhart and D. Zipser, "Feature discovery by competitive learning," Cognitive Sci., vol. 9, pp. 75-112, 1985.
7. G. A. Carpenter and S. Grossberg, "ART2: Self-organization of stable category recognition codes for analog input patterns," Appl. Opt. I, vol. 26, no. 23, pp. 4919-4930, Dec. 1987.
8. N. R. Pal, J. C. Bezdek, and E. C. K. Tsao, "Generalized clustering networks and Kohonen's self-organizing scheme," IEEE Trans. Neural Networks, vol. 4, pp. 549-557, July 1993.
9. N. Karayiannis, J. C. Bezdek, N. R. Pal, R. J. Hathaway, and P.-I. Pai, "Repairs to GLVQ: A new family of competitive learning schemes," IEEE Trans. Neural Networks, vol. 7, pp. 1062-1071, Sept. 1996.
10. S. C. Ahalt, A. K. Krishnamurthy, P. Chen, and D. E. Melton, "Competitive learning algorithms for vector quantization," Neural Networks, vol. 3, pp. 277-290, 1990.
11. A. K. Krishnamurthy, S. C. Ahalt, D. E. Melton, and P. Chen, "Neural networks for vector quantization of speech and images," IEEE J. Select. Areas Commun., vol. 8, pp. 1449-1457, Oct. 1990.
12. T. M. Martinetz, S. G. Berkovich, and K. J. Schulten, "Neural-gas network for vector quantization and its application to time-series prediction," IEEE Trans. Neural Networks, vol. 4, pp. 558-569, July 1993.
13. D. Butler and J. Jiang, "Distortion equalized fuzzy competitive learning for image data vector quantization," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP'96), London, U.K., May 1996, pp. 3390-3393.
14. C. S.-T. Choy and W.-C. Siu, "Distortion sensitive competitive learning for vector quantizer design," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP'97), Munich, Germany, Apr. 1997, pp. 3405-3408.
15. D. DeSieno, "Adding a conscience to competitive learning," in Proc. IEEE Int. Conf. Neural Networks, 1988, vol. 1, pp. 117-124.
16. L.-H. Chen and S. Chang, "An adaptive conscientious competitive learning algorithm and its applications," Pattern Recognitions, vol. 27, no. 12, pp. 1787-1813, 1994.
17. S. Grossberg, "On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks," J. Statist. Phys., vol. 1, no. 2, pp. 319-350, 1969.
18. _, "Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors," Biol. Cybern., vol. 23, pp. 121-134, 1976.
19. G. C. Goodwin and K. S. Sin, Adaptive Filtering Prediction and Control. Englewood Cliffs, NJ: Prentice-Hall, 1984.
20. E. B. Kosmatopoulos and M. A. Christodoulou, "Convergence properties of a class of learning vector quantization algorithms," IEEE Trans. Image Processing, vol. 5, pp. 361-368, Feb. 1996.
21. A. I. Gonzalez, M. Grana, and A. Dànjou, "An analysis of the GLVQ algorithm," IEEE Trans. Neural Networks, vol. 6, pp. 1012-1016, July 1995.
22. R. M. Gray, "Vector quantization," IEEE ASSP Mag., vol. 1, pp. 4-29, Apr. 1984.
23. D. M. Clark and K. Ravishankar, "A convergence theorem for Grossberg learning," Neural Networks, vol. 3, pp. 87-92, 1990.
24. J. A. Kangas, T. K. Kohonen, and J. T. Laaksonen, "Variants of self-organizing maps," IEEE Trans. Neural Networks, vol. 1, pp. 93-99, Mar. 1990.
25. L. A. Zadeh, "Fuzzy sets," Inform. Contr., vol. 8, pp. 338-353, 1965.
26. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
27. F. L. Chung and T. Lee, "Fuzzy competitive learning," Neural Networks, vol. 7, no. 3, pp. 539-551, 1994.
28. C. Zhu, L. H. Li, T. J. Wang, and Z. Y. He, "Partial-distortion-weighted fuzzy competitive learning algorithm for vector quantization," Electron. Lett., vol. 30, no. 6, pp. 505-506, Mar. 1994.
29. N. B. Karayiannis and P.-I. Pai, "Fuzzy algorithms for learning vector quantization," IEEE Trans. Neural Networks, vol. 7, pp. 1196-1211, Sept. 1996.
30. A. Gersho, "Asymptotically optimal block quantization," IEEE Trans. Inform. Theory, vol. 25, pp. 373-380, July 1979.