Similarities, dissimilarities and types of inner products for data analysis in the context of machine learning a mathematical characterization

Abstract

Data dissimilarities and similarities are the key ingredients of machine learning. We give a mathematical characterization and classification of those measures based on structural properties also involving psychological-cognitive aspects of similarity determination, and investigate admissible conversions. Finally, we discuss some consequences of the obtained taxonomy and their implications for machine learning algorithms.