Spectral embedded clustering

Nie, F; Xu, D; Tsang, IW; Zhang, C

Spectral embedded clustering

Nie, F Xu, D

Tsang, IW

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2009, pp. 1181 - 1186
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Nie, F	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Zhang, C	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2009, pp. 1181 - 1186	en_US
dc.identifier.isbn	9781577354260	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30918
dc.description.abstract	In this paper, we propose a new spectral clustering method, referred to as Spectral Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clustering as well as control the mismatch between the cluster assignment matrix and the low dimensional embedded representation of the data. SEC is based on the observation that the cluster assignment matrix of high dimensional data can be represented by a low dimensional linear mapping of data. We also discover the connection between SEC and other clustering methods, such as spectral clustering, Clustering with local and global regularization, K-means and Discriminative K-means. The experiments on many realworld data sets show that SEC significantly outperforms the existing spectral clustering methods as well as K-means clustering related methods.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Spectral embedded clustering	en_US
dc.type	Conference Proceeding
dc.location.activity	Pasadena, CA, USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we propose a new spectral clustering method, referred to as Spectral Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clustering as well as control the mismatch between the cluster assignment matrix and the low dimensional embedded representation of the data. SEC is based on the observation that the cluster assignment matrix of high dimensional data can be represented by a low dimensional linear mapping of data. We also discover the connection between SEC and other clustering methods, such as spectral clustering, Clustering with local and global regularization, K-means and Discriminative K-means. The experiments on many realworld data sets show that SEC significantly outperforms the existing spectral clustering methods as well as K-means clustering related methods.

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http://hdl.handle.net/10453/30918