Classifying token frequencies using angular Minkowski $p$-distance
Oliver Urs Lenz Chris Cornelis
Angular Minkowski $p$-distance is a dissimilarity measure that is obtained by
replacing Euclidean distance in the definition of cosine dissimilarity with
other Minkowski $p$-distances. Cosine dissimilarity is frequently used with
datasets containing token frequencies, and angular Minkowski $p$-distance may
potentially be an even better choice for certain tasks. In a case study based
on the 20-newsgroups dataset, we evaluate clasification performance for
classical weighted nearest neighbours, as well as fuzzy rough nearest
neighbours. In addition, we analyse the relationship between the hyperparameter
$p$, the dimensionality $m$ of the dataset, the number of neighbours $k$, the
choice of weights and the choice of classifier. We conclude that it is possible
to obtain substantially higher classification performance with angular
Minkowski $p$-distance with suitable values for $p$ than with classical cosine
dissimilarity.