Bayesian robust PCA for incomplete data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5441, Issue , 2009, Pages 66-73

  Luttinen, Jaakko ^a   Ilin, Alexander ^a   Karhunen, Juha ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION METHODS; BAYESIAN; DATA DIMENSIONS; DATA SETS; DATA VECTORS; HEAVY-TAILED; INCOMPLETE DATA; MISSING VALUES; NOISE DISTRIBUTION; NOISE MODELS; NOISY DATA; OBSERVATION NOISE; POSTERIOR EVALUATION; PROBABILISTIC MODELS; PROBABILISTIC PCA; REAL-WORLD; ROBUST PCA; ROBUST PRINCIPAL COMPONENT ANALYSIS; VARIATIONAL BAYESIAN;

APPROXIMATION THEORY; BAYESIAN NETWORKS; HEMODYNAMICS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DISTRIBUTIONS; ROBUST CONTROL;

INDEPENDENT COMPONENT ANALYSIS;

EID: 67149131576     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-00599-2_9     Document Type: Conference Paper

References (12)

1. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)
2. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. J. Wiley, Chichester (2001)
3. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Secaucus (2006)
4. Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley &Sons, Inc., New York (2002)
5. Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society Series B 61(3), 611-622 (1999)
6. Zhao, J., Jiang, Q.: Probabilistic PCA for t distributions. Neurocomputing 69, 2217-2226 (2006)
7. Archambeau, C., Delannay, N., Verleysen, M.: Robust probabilistic projections. In: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 33-40. ACM, New York (2007)
8. Gao, J.: Robust L1 principal component analysis and its Bayesian variational inference. Neural Computation 20(2), 555-572 (2008)
9. Ilin, A., Valpola, H., Oja, E.: Exploratory analysis of climate data using source separation methods. Neural Networks 19(2), 155-167 (2006)
10. Bishop, C.M.: Variational principal components. In: Proceedings of the 9th International Conference on Artificial Neural Networks (ICANN 1999), vol. 1, pp. 509-514 (1999)
11. Ilin, A., Raiko, T.: Practical approaches to principal component analysis in the presence of missing values. Technical Report TKK-ICS-R6, Helsinki University of Technology, Espoo, Finland (2008), http://www.cis.hut.fi/alexilin/
12. Liu, C., Rubin, D.B.: ML estimation of the t distribution using EM and its extensions, ECM and ECME. Statistica Sinica 5, 19-39 (1995)