Supervised Categorical Metric Learning with Schatten p-Norms

TL;DR

This paper introduces CPML, a novel metric learning method for categorical data using Schatten p-norms, achieving improved efficiency and accuracy over existing approaches.

Contribution

It proposes a new categorical metric learning approach utilizing Value Distance Metric and Schatten p-norm regularization, with theoretical generalization bounds and empirical validation.

Findings

Enhanced prediction accuracy on categorical datasets

Reduced computational time compared to existing methods

Theoretical generalization bounds for the proposed regularizer

Abstract

Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development on categorical data still needs further exploration. In this paper, we propose a method, called CPML for \emph{categorical projected metric learning}, that tries to efficiently~(i.e. less computational time and better prediction accuracy) address the problem of metric learning in categorical data. We make use of the Value Distance Metric to represent our data and propose new distances based on this representation. We then show how to efficiently learn new metrics. We also generalize several previous regularizers through the Schatten $p$-norm and provides a generalization bound for it that complements the standard generalization bound for metric learning. Experimental results show that our method provides