Discriminative k-means clustering

Author

Arandjelovic, Ognjen

Author_Institution

Centre for Pattern Recognition & Data Analytics, Deakin Univ., Geelong, VIC, Australia

fYear

2013

fDate

4-9 Aug. 2013

Firstpage

1

Lastpage

7

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.

Keywords

pattern clustering; cluster structure; clustering algorithm; discriminative k-means clustering; face recognition; k-means algorithm; negatively labelled data; partitional clustering method; person appearance; positively labelled data; Approximation algorithms; Clustering algorithms; Convergence; Face; Face recognition; Partitioning algorithms; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2013 International Joint Conference on

Conference_Location

Dallas, TX

ISSN

2161-4393

Print_ISBN

978-1-4673-6128-6

Type

conf

DOI

10.1109/IJCNN.2013.6707038

Filename

6707038