Discriminative k-means clustering

Oggie Arandelovic

doi:10.1109/IJCNN.2013.6707038

Discriminative k-means clustering

Oggie Arandelovic^*

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
DOIs	https://doi.org/10.1109/IJCNN.2013.6707038
Publication status	Published - 2013
Event	2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States Duration: 4 Aug 2013 → 9 Aug 2013

Conference

Conference	2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/Territory	United States
City	Dallas, TX
Period	4/08/13 → 9/08/13

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10.1109/IJCNN.2013.6707038

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title = "Discriminative k-means 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.",

author = "Oggie Arandelovic",

year = "2013",

doi = "10.1109/IJCNN.2013.6707038",

language = "English",

isbn = "9781467361293",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "2013 International Joint Conference on Neural Networks, IJCNN 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

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PY - 2013

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N2 - 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.

AB - 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.

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U2 - 10.1109/IJCNN.2013.6707038

DO - 10.1109/IJCNN.2013.6707038

M3 - Conference contribution

AN - SCOPUS:84893550189

SN - 9781467361293

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013

Y2 - 4 August 2013 through 9 August 2013

ER -

Discriminative k-means clustering

Abstract

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