Principal Component Extraction Using Recursive Least Squares Learning

IEEE Transactions on Neural Networks

Volumn 6, Issue 2, 1995, Pages 457-469

  Bannour, Sami ^a   Azimi Sadjadi, Mahmood R ^a  

^a Veterinary Dentistry and Oral Surgery   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; ERROR ANALYSIS; FOURIER TRANSFORMS; IMAGE ANALYSIS; IMAGE PROCESSING; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; RANDOM PROCESSES; VECTORS;

MEAN SQUARED ERRORS; NEURONS;

NEURAL NETWORKS;

EID: 0029267855     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.363480     Document Type: Article

References (18)

1. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
2. E. Oja, “A simplified neuron as a principal component analyzer,” J. Math. Biology, vol. 15, pp. 267-273, 1982.
3. T. D. Sanger, “Optimal unsupervised learning in a single-layer linear feedforward neural network,” Neural Networks, vol. 2, pp. 459-473, 1989.
4. P. Foldiak, “Adaptive network for optimal linear feature extraction,” in Int. Joint Conf. Neural Networks, Washington, DC, 1989, pp. 1401-1406.
5. S. Y. Kung and K. I. Diamantaras, “A neural network learning algorithm for adaptive principal component extraction,” in Proc. ICASSP, Albuquerque, Apr. 1990, pp. 861-864.
6. S. Haykin, Adaptive Filter Theory. Englewood Cliffs, NJ: Prentice-Hall, second edition, 1991.
7. B. Widrow and S. D. Stearns, Adaptive Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1985.
8. H. Bourland and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biological Cybernetics, vol. 59, pp. 291-294, 1988.
9. S. Bannour and M. R. Azimi-Sadjadi, “An adaptive approach for optimal data reduction using recursive least squares learning method,” in Proc. ICASSP 92), San Francisco, CA, May 23-26, 1992, pp. 11-297-300
10. “Principal component extraction using recursive least squares learning method,” in Proc. 1991 Int. Joint Conf. Neural Networks (IJCNN 91), pp. 2110-2115, Singapore, Nov. 1991
11. B. N. Parlett, The Symmetric Eigenvalue Problem. Englewood Cliffs, NJ: Prentice-Hall, 1980.
12. H. Stark and J. W. Woods, Probability and Random Processes and Estimation Theory for Engineers. Englewood Cliffs, NJ: Prentice-Hall, 1986.
13. P. Baldi and K. Hornik, “Neural networks and principal component analysis: Learning from examples without local minima,” Neural Networks, vol. 2, pp. 53-58, 1989.
14. J. B. Brockwell and R. A. Davis, Time Series: Theory and Methods. New York: Springer-Verlag, 1986.
15. M. R. Azimi-Sadjadi, D. E. Poole, S. Sheedvash, K. D. Sherbondy, and S. A. Strieker, “Detection and classification of barried dielectric anomalies using a seperated aperture sensor and a neural network discriminator,” IEEE Trans. on Instrum. Meas. vol. 41, no. 1, pp. 137-143, Feb. 1991.
16. J. W. Goodman, “Some fundamental properties of speckle,” J. Opt. Soc. Am., vol. 6, pp. 1145-1150, Nov. 1976.
17. M. R. Azimi-Sadjadi and S. Bannour, “Two-dimensional adaptive block Kalman filtering of SAR imagery,” IEEE Trans. on Geosc. Remote Sensing, vol. 29, no. 5, pp. 742-753, Sept. 1991.
18. F. Palmieri and J. Zhu, “A comparison of two-eigen-networks,” in Proc. IJCNN, pp. 193-199, Seattle, WA, July 1991.