About Interpretable Learning Rules for Vector Quantizers - A Methodological Approach

In this work, we will investigate two approaches to deploy learning rules. A combination of these approaches is used to create a generic learning rule for prototype-based models with the emphasis on interpretability. In this regard, we will show how the learning rules are associated to the underlying decision making of such models. Moreover, the work concludes by giving possible interpretations of these rules and anchor points for developing related explanations and designing comprehensible learning rules.