An expectation-maximization approach to nonlinear component analysis

Neural Computation

Volumn 13, Issue 3, 2001, Pages 505-510

  Rosipal, Roman ^a   Girolami, Mark ^a  

^a UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0006019710     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976601300014439     Document Type: Article

References (6)

1. Jolliffe, I. T. (1986). Principal component analysis. New York: Springer-Verlag.
2. Rosipal, R., Girolami, M., & Trejo, L. (2000). Kernel PCA for feature extraction and de-noising in non-linear regression (Tech. Rep. No. 4). Paisley, Scotland: CIS Department, University of Paisley.
3. Roweis, S., & Ghahramani, Z. (1999). A unifying review of linear gaussian models. Neural Computation, 11, 305-345.
4. Schölkopf, B., Burges, C., & Smola, A. (1999). Advances in kernel methods - Support vector learning. Cambridge, MA: MIT Press.
5. Schölkopf, B., Smola, A., & Müller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1219.
6. Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. J. R. Statist. Soc. B, 61, 611-622.