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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
Oja, E.:
Neural networks, principal components, and subspaces.
International Journal of Neural Systems 1(1) (1989) 61-68

8
Tipping, M., Bishop, C.:
Probabilistic principal component analysis.
Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61(3) (1999) 611-622

9
Grung, B., Manne, R.:
Missing values in principal components analysis.
Chemometrics and Intelligent Laboratory Systems 42(1) (August 1998) 125-139

10
Bishop, C.:
Variational principal components.
In: Proc. 9th Int. Conf. on Artificial Neural Networks (ICANN99). (1999) 509-514

11
Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K., Ishii, S.:
A Bayesian missing value estimation method for gene expression profile data.
Bioinformatics 19(16) (2003) 2088-2096

12
Raiko, T., Valpola, H., Harva, M., Karhunen, J.:
Building blocks for variational Bayesian learning of latent variable models.
Journal of Machine Learning Research 8(Jan) (2007) 155-201

13
Netflix:
Netflix prize webpage (2007) http://www.netflixprize.com/.

14
Funk, S.:
Netflix update: Try this at home.
Available at http://sifter.org/~simon/journal/20061211.html (December 2006)

15
Salakhutdinov, R., Mnih, A., Hinton, G.:
Restricted Boltzmann machines for collaborative filtering.
In: Proc. Int. Conf. on Machine Learning. (2007) To appear.



Tapani Raiko 2007-07-16