(no subject)

[am]

PCA with missing data:
Raiko, T., Ilin, A., Karhunen, J.
(2007) "Principal Component Analysis
for Large Scale Problems with Lots
of Missing Values" ECML2007, 691-698.
Raiko, T., Valpola, H., Harva, M.,
Karhunen, J. (2007) "Building blocks
for variational Bayesian learning of
latent variable models" J. of M.L.
Res. 8, 155-201.
Ilin, A., Raiko, T. (2010) "Practical
Approaches to Principal Component
Analysis in the Presence of Missing
Values" J. of M.L. Res., 11, 1957-2000.
Grung, B., Manne, R. (1998) "Missing
values in principal components analysis"
Chemometrics and Intelligent Laboratory
Systems 42(1), 125-139.
Oba, S., Sato, M., Takemasa, I., Monden, M.,
Matsubara, K., Ishii, S. (2003) "A Bayesian
missing value estimation method for gene
expression profile data" Bioinf.19(16), 2088-96.

“Low-rank and sparse”
Robust PCA handles out-
liers and missing data.
( Bilinear models.)
( Netflix competition.)

Probabilistic PCA:
Tipping, M.E., Bishop, C.M. (1999)
"Probabilistic principal component
analysis." J. of the Royal Stat. Soc.,
Ser.B 21 (3), 611-622.
Bishop, C.M. (1999) "Bayesian PCA."
Adv. NIPS, 11: 382-388. MIT Press.
Bishop, C.M. (1999) "Variational
principal components." ICANN'99, 509-514.

Update:
"Large-scale Parallel
Collaborative Filtering
for the Netflix Prize."

TDLS Classics - "Collabo-
rative Filtering for the
Netflix Prize", P. 1, 2.