Abstract
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 dimensional 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.
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