Discriminative k-means clustering

TL;DR

This paper introduces a discriminative k-means clustering algorithm that leverages labeled data to distinguish between positive and negative examples, demonstrated on face recognition tasks.

Contribution

It extends traditional k-means to incorporate label information, enabling discrimination between classes within the clustering process.

Findings

Effective separation of positive and negative data in face recognition

Maintains simplicity and efficiency of standard k-means

Demonstrates practical utility in discriminative clustering

Abstract

The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.