We address the problem of defining similarity between vectors of possibly dependent categorical variables by deriving formulas for the Fisher kernel for Bayesian networks. While both Bayesian networks and Fisher kernels are established techniques, this result does not seem to appear in the literature. Such a kernel naturally opens up the possibility to conduct kernel-based analyses in completely categorical feature spaces with dependent features. In the experiments, we illustrate the nature of similarity measured by the kernel by applying it to finding sets of observations that we see as representative for the underlying Bayesian network model.
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