A Convex Formulation for Spectral Shrunk Clustering

Chang, X; Nie, F; Ma, Z; Yang, Y; Zhou, X

A Convex Formulation for Spectral Shrunk Clustering

Chang, X

Nie, F Ma, Z Yang, Y Zhou, X

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Publisher:: Association for the Advancement of Artificial Intelligence (AAAI)
Publication Type:: Journal Article
Citation:: Proceedings of the AAAI Conference on Artificial Intelligence, 29, (1)

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Field	Value	Language
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Nie, F
dc.contributor.author	Ma, Z
dc.contributor.author	Yang, Y
dc.contributor.author	Zhou, X
dc.date.accessioned	2023-03-31T11:06:19Z
dc.date.available	2023-03-31T11:06:19Z
dc.identifier.citation	Proceedings of the AAAI Conference on Artificial Intelligence, 29, (1)
dc.identifier.issn	2159-5399
dc.identifier.issn	2374-3468
dc.identifier.uri	http://hdl.handle.net/10453/169018
dc.description.abstract	<jats:p> Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance. </jats:p>
dc.language	en
dc.publisher	Association for the Advancement of Artificial Intelligence (AAAI)
dc.relation.ispartof	Proceedings of the AAAI Conference on Artificial Intelligence
dc.relation.isbasedon	10.1609/aaai.v29i1.9606
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Convex Formulation for Spectral Shrunk Clustering
dc.type	Journal Article
utslib.citation.volume	29
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 - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T11:06:18Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	29
utslib.citation.issue	1

Abstract:

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.

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http://hdl.handle.net/10453/169018