Generic probabilistic prototype based classification of vectorial and proximity data

•Introduction of relevance and matrix learning to discriminative probabilistic prototype learning.•Derivation of proximity learning including dissimilarity and kernel matrices for discriminative probabilistic prototype learning.•Analysis of discriminative probabilistic prototype learning in the context of unsafe label information.

In supervised learning probabilistic models are attractive to define discriminative models in a rigid mathematical framework. More recently, prototype approaches, known for compact and efficient models, were defined in a probabilistic setting, but are limited to metric vectorial spaces. Here we propose a generalization of the discriminative probabilistic prototype learning algorithm for arbitrary proximity data, widely applicable to a multitude of data analysis tasks. We extend the algorithm to incorporate adaptive distance measures, kernels and non-metric proximities in a full probabilistic framework.