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Bibliography

1
Pearson, K.:
On lines and planes of closest fit to systems of points in space.
Philosophical Magazine 2(6) (1901) 559-572

2
Jolliffe, I.:
Principal Component Analysis.
Springer-Verlag (1986)

3
Bishop, C.:
Pattern Recognition and Machine Learning.
Springer-Verlag (2006)

4
Diamantaras, K., Kung, S.:
Principal Component Neural Networks - Theory and Application.
Wiley (1996)

5
Haykin, S.:
Modern Filters.
Macmillan (1989)

6
Cichocki, A., Amari, S.:
Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications.
Wiley (2002)

7
Roweis, S.:
EM algorithms for PCA and SPCA.
In: Advances in Neural Information Processing Systems 10, Cambridge, MA, MIT Press (1998) 626-632

8
Karhunen, J., Oja, E.:
New methods for stochastic approximation of truncated Karhunen-Loeve expansions.
In: Proceedings of the 6th International Conference on Pattern Recognition, Springer-Verlag (1982) 550-553

9
Oja, E.:
Subspace Methods of Pattern Recognition.
Research Studies Press and J. Wiley (1983)

10
Amari, S.:
Natural gradient works efficiently in learning.
Neural Computation 10(2) (1998) 251-276

11
Grung, B., Manne, R.:
Missing values in principal components analysis.
Chemometrics and Intelligent Laboratory Systems 42(1) (August 1998) 125-139

12
Raiko, T., Ilin, A., Karhunen, J.:
Principal component analysis for large scale problems with lots of missing values.
In: Proceedings of the 18th European Conference on Machine Learning (ECML 2007), Springer-Verlag (September 2007) 691-698

13
Bishop, C.:
Variational principal components.
In: Proceedings of the 9th International Conference on Artificial Neural Networks (ICANN99). (1999) 509-514

14
Netflix:
Netflix prize webpage (2007) http://www.netflixprize.com/.

15
Funk, S.:
Netflix update: Try this at home.
Available at http://sifter.org/~simon/journal/20061211.html (December 2006)

16
Salakhutdinov, R., Mnih, A., Hinton, G.:
Restricted Boltzmann machines for collaborative filtering.
In: Proceedings of the International Conference on Machine Learning. (2007)



Tapani Raiko 2007-09-11