Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems

Evolving Systems

Volumn 1, Issue 1, 2010, Pages 17-27

  Hisada, Masayuki ^a   Ozawa, Seiichi ^a   Zhang, Kau ^a   Kasabov, Nikola ^b  

^a KOBE UNIVERSITY   (Japan)

^b AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

Author keywords

Feature extraction; Incremental learning; Linear discriminant analysis; Multitask learning

Indexed keywords

CLASS SEPARABILITY; DISCRIMINANT VECTORS; FEATURE SPACE; INCREMENTAL LEARNING; INCREMENTAL LINEAR DISCRIMINANT ANALYSIS; KNOWLEDGE TRANSFER; LINEAR DISCRIMINANT ANALYSIS; MULTITASK LEARNING; PATTERN RECOGNITION PROBLEMS; RECOGNITION ACCURACY; TRAINING DATA;

DISCRIMINANT ANALYSIS; FACE RECOGNITION; KNOWLEDGE MANAGEMENT; VECTOR SPACES;

FEATURE EXTRACTION;

EID: 79952316981     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-010-9000-3     Document Type: Article

References (21)

1. Abu-Mostafa YS (1989) Learning from hints in neural networks. J Complex 6:192-198.
2. Argyriou A, Evgeniou T, Pontil M (2007) Multi-task feature learning. In: Schölkopf B et al (eds) Advances in neural information processing systems 19, MIT Press, pp 41-48.
3. Blake C, Keogh E, Merz C (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html.
4. Caruana R (1995) Learning many related tasks at the same time with backpropagation. In: Tesauro G et al (eds) Advances in neural information processing systems 7, MIT Press, pp 657-664.
5. Caruana R (1997) Multitask learning. Mach Learn 28:41-75.
6. Carpenter GA, Grossberg S (1998) The ART of adaptive pattern recognition by a self-organizing neural network. IEEE Comput 21:77-88.
7. Cauwenberghs G, Poggio T (2000) Incremental and decremental support vector machine learning. In: Leen T et al (eds) Advances in neural information processing systems 13, MIT Press.
8. Kasabov N (2001) Evolving fuzzy neural networks for supervised/unsupervised on-line knowledge-based learning. IEEE Trans Syst Man Cybern B 31:902-918.
9. Kasabov N (2002) Evolving connectionist systems: methods and applications in bioinformatics, brain study and intelligent machines. Springer, London.
10. Okada T and Tomita S (1985) An optimal orthonormal system for discriminant analysis. Pattern Recognit 18:139-144.
11. Ozawa S, Toh SL, Abe S, Pang S, Kasabov N (2005) Incremental learning of feature space and classifier for face recognition. Neural Netw 18:575-584.
12. Ozawa S, Pang S, Kasabov N (2008) Incremental learning of chunk data for on-line pattern classification systems. IEEE Trans on Neural Netw 19:430-445.
13. Ozawa S, Roy A, Roussinov D (2009) A multitask learning model for online pattern recognition. IEEE Trans Neural Netw 20:1061-1074.
14. Pang S, Ozawa S, Kasabov N (2005) Incremental linear discriminant analysis for classification of data streams. IEEE Trans Syst Man Cybern B 35:905-914.
15. Platt J (1991) A resource allocating network for function interpolation. Neural Comput 3:213-225.
16. Roy A (2003) Neural networks: how do we make a widely used technology out of it? IEEE Neural Netw Soc Newsl 1:8-12.
17. Silver D and Mercer R (2001) Selective functional transfer: inductive bias from related tasks. In: Proc IASTED Int Conf on Artif Intell and Soft Comput, pp 182-189.
18. Takeuchi Y, Ozawa S, Abe S (2007) An efficient incremental kernel principal component analysis for online feature selection. In: Proc Int Joint Conf on Neural Netwo 2007, pp 1603-1608.
19. Thrun S, Pratt L (1998) Learning to learn, Kluwer, Boston.
20. Xiang C, Fan X, Lee T (2006) Face recognition using recursive fisher linear discriminant. IEEE Trans on Image Process 15:2097-2105.
21. Zheng W (2006) Class-incremental generalized discriminant analysis. Neural Comput 18:979-1006.