Description: we present a nonlinear version of the
well-known anomaly detection method referred to as the RX-algorithm.
Extending this algorithm to a feature space associated with
the original input space via a certain nonlinear mapping function
can provide a nonlinear version of the RX-algorithm. This nonlinear
RX-algorithm, referred to as the kernel RX-algorithm, is
basically intractable mainly due to the high dimensionality of the
feature space produced by the nonlinear mapping function. However,
in this paper it is shown that the kernel RX-algorithm can
easily be implemented by kernelizing the RX-algorithm
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