Fast adaptive LDA using quasi-Newton algorithm

Pattern Recognition Letters

Volumn 28, Issue 5, 2007, Pages 613-621

  Abrishami Moghaddam, H ^a   Matinfar, M ^a  

^a K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Adaptive linear discriminant analysis; Gradient descent optimization; Newton Raphson optimization; Secant method; Self organizing neural network

Indexed keywords

COMPUTATION THEORY; NEURAL NETWORKS; OPTIMAL CONTROL SYSTEMS; OPTIMIZATION; PARAMETER ESTIMATION; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS;

ADAPTIVE LINEAR DISCRIMINANT ANALYSIS; GRADIENT DESCENT OPTIMIZATION; NEWTON-RAPHSON OPTIMIZATION; SECANT METHOD; SELF-ORGANIZING NEURAL NETWORK;

ADAPTIVE ALGORITHMS;

EID: 33846215030     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2006.10.011     Document Type: Article

References (17)

1. Abrishami Moghaddam H., and Amiri-Zadeh Kh. Fast adaptive algorithms and networks for class-separability features. Pattern Recognition 36 8 (2003) 1695-1702
2. Abrishami Moghaddam H., Matinfar M., Sajad Sadough S.M., and Amiri Zadeh Kh. Algorithms and networks for accelerated convergence of adaptive LDA. Pattern Recognition 38 4 (2005) 473-483
3. Benveniste A., Metivier M., and Priouret P. Adaptive Algorithms and Stochastic Approximations (1990), Springer-Verlag
4. Chatterjee C., and Roychowdhury V.P. On self-organizing algorithms and networks for class-separability features. IEEE Trans. Neural Net. 8 3 (1997) 663-678
5. Dennis J.E., and Schnabel R.B. Numerical Methods for Unconstrained Optimization and Nonlinear Equations (1983), Prentice-Hall
6. Fukunaga k. Introduction to Statistical Pattern Recognition. 2nd ed. (1990), Academic Press
7. Gill P.E., Murray W., and Wright M.H. Practical Optimization (1981), Academic Press
8. Haykin S. Neural Networks: A Comprehensive Foundation (1994), Macmillan
9. Hongo, H., Yasumoto, M., Niwa, Y., Yamamoto, K., 2002. Hierarchical face recognition using an adaptive discriminant space. In: Proceedings of IEEE International Conference on Computers, Communications, Control and Power Engineering (TENCON'02), vol. 1, pp. 523-528.
10. Ljung L. Analysis of recursive stochastic algorithms. IEEE Trans. Automat. Cont. 22 4 (1997) 551-575
11. Luenberger D.G. Linear and Nonlinear Programming (1984), Addison-Wesley, Reading, MA
12. Mao J., and Jain A.K. Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Net. 6 2 (1995) 296-317
13. Miao Y., and Hua Y. Fast subspace tracking and neural network learning by a novel information criterion. IEEE Trans. Signal Process. 46 7 (1998) 1967-1979
14. Okada T., and Tomita S. An optimal orthonormal system for discriminant analysis. Pattern Recognition 18 2 (1985) 139-144
15. Rao Y.N., Principe J.C., and Wong T.F. Fast RLS-like algorithm for generalized eigendecomposition and its applications. J. VLSI Signal Process. Syst. 37 2-3 (2004) 333-344
16. Sanger T.D. Optimal unsupervised learning in a single-layer linear feed-forward neural network. Neural Networks 2 6 (1989) 459-473
17. Widrow B., and Stearns S. Adaptive Signal Processing (1985), Prentice-Hall