Using Enriched Category Theory to Construct the Nearest Neighbour
  Classification Algorithm
Matthew Pugh Jo Grundy Corina Cirstea Nick Harris
Exploring whether Enriched Category Theory could provide the foundation of an
alternative approach to Machine Learning. This paper is the first to construct
and motivate a Machine Learning algorithm solely with Enriched Category Theory.
In order to supplement evidence that Category Theory can be used to motivate
robust and explainable algorithms, it is shown that a series of reasonable
assumptions about a dataset lead to the construction of the Nearest Neighbours
Algorithm. In particular, as an extension of the original dataset using
profunctors in the category of Lawvere metric spaces. This leads to a
definition of an Enriched Nearest Neighbours Algorithm, which consequently also
produces an enriched form of the Voronoi diagram. This paper is intended to be
accessible without any knowledge of Category Theory