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Bibliography

1
S. Mika, B. Schölkopf, A. Smola, K.-R. Müller, M. Scholz, and G. Rätsch.

Kernel PCA and de-noising in feature spaces.
In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 536-542. MIT Press, Cambridge, MA, 1999.
Download from HERE.
 
2
P. M. Murphy and D. W. Aha.

UCI Repository of machine learning databases, 1994.
Available from:
www.ics.uci.edu/~mlearn/MLRepository.html.
 
3
B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors.

Advances in Kernel Methods - Support Vector Learning.
MIT Press, Cambridge, MA, 1999.
 
4
B. Schölkopf, S. Mika, A. Smola, G. Rätsch, and K.-R. Müller.

Kernel PCA pattern reconstruction via approximate pre-images.
In L. Niklasson, M. Bodén, and T. Ziemke, editors, Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing, pages 147-152. Springer Verlag, Berlin, 1998.
Download from HERE.
 
5
B. Schölkopf, A. Smola, and K.-R. Müller.

Nonlinear component analysis as a kernel eigenvalue problem.
Neural Computation, 10:1299-1319, 1998.
Download from HERE.
 
6
B. Schölkopf, A. Smola, and K.-R. Müller.

Kernel principal component analysis.
In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 327-352. MIT Press, Cambridge, MA, 1999.
 
 
7
C. J. Twining and C. J. Taylor.

Kernel principal component analysis and the construction of non-linear active shape models.
In T. Cootes and C. J. Taylor, editors, Proceedings of BMVC20001, Manchester, UK, volume 1, pages 23-32. BMVA.



Carole Twining

2001-10-02