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