Abstract
Abstract
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.
1. Difference in female/male salary: 33/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: uncalculable
4. Number of employees in under-represented gender in 10 highest salaries: 0/10 points
NAVER France targets (with respect to the 2022 index) are as follows:
En 2022, NAVER France a obtenu les notes suivantes pour chacun des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 33 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : non calculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 0 sur 10 points
Les objectifs de progression pour l’index 2022 de NAVER France sont :
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