Methods beyond neural scaling laws for beating power scaling laws in machine learning havebecome topical for high-performance machine learning models. Nearest Prototype Classifiers (NPCs)introduce a category of machine learning models known for their interpretability. However, theperformance of NPCs is frequently impacted by large datasets that scale to high dimensions. Wesurmount the performance hurdle by employing self-supervised prototype-based learning metrics tointelligently prune datasets of varying sizes, encompassing low and high dimensions. This processaims to enhance the robustification and certification of NPCs within the framework of the LearningVector Quantization (LVQ) family of algorithms, utilizing Crammer normalization for arbitrarysemi-norms (semi-metrics). The numerical evaluation of outcomes reveals that NPCs trained withpruned datasets demonstrate sustained or enhanced performance compared to instances where trainingis conducted with full datasets. The self-supervised prototype-based metric (SSL) and the Perceptual-SSL (P-SSL) utilized in this study remain unaffected by the intricacies of optimal hyperparameterselection. Consequently, data pruning metrics can be seamlessly integrated with triplet loss trainingto assess the empirical and guaranteed robustness of Lp-NPCs and Perceptual-NPCs (P-NPCs),facilitating the curation of datasets that contribute to research in applied machine learning.