Theoretical properties of distance distributions and novel metrics for nearest-neighbor feature selection.

ABSTRACT: The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. Recent work to improve nearest-neighbor feature selection algorithms has focused on new neighborhood estimation methods and distance metrics. However, little attention has been given to the distributional properties of pairwise distances as a function of the metric or data type. Thus, we derive general analytical expressions for the mean and variance of pairwise distances for Lq metrics for normal and uniform random data with p attributes and m instances. The distribution moment formulas and detailed derivations provide a resource for understanding the distance properties for metrics and data types commonly us

SUBMITTER: Dawkins BA 

PROVIDER: S-EPMC7870093 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature