Rosipal, R and Girolami, M (2001) An expectation-maximization approach to nonlinear component analysis. NEURAL COMPUT , 13 (3) 505 - 510.
Full text not available from this repository.
The proposal of considering nonlinear principal component analysis as a kernel eigenvalue problem has provided an extremely powerful method of extracting nonlinear features for a number of classification and regression applications. Whereas the utilization of Mercer kernels makes the problem of computing principal components in, possibly, infinite-demensional feature spaces tractable, there are still the attendant numerical problems of diagonalizing large matrices. In this contribution, we propose an expectation-maximization approach for performing kernel principal component analysis and show this to be a computationally efficient method, especially when the number of data points is large.
|Title:||An expectation-maximization approach to nonlinear component analysis|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
Archive Staff Only: edit this record