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Conference Paper: Spectral embedded clustering
Title | Spectral embedded clustering |
---|---|
Authors | |
Issue Date | 2009 |
Citation | IJCAI International Joint Conference on Artificial Intelligence, 2009, p. 1181-1186 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/321425 |
ISSN | 2020 SCImago Journal Rankings: 0.649 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Nie, Feiping | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Tsang, Ivor W. | - |
dc.contributor.author | Zhang, Changshui | - |
dc.date.accessioned | 2022-11-03T02:18:50Z | - |
dc.date.available | 2022-11-03T02:18:50Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | IJCAI International Joint Conference on Artificial Intelligence, 2009, p. 1181-1186 | - |
dc.identifier.issn | 1045-0823 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321425 | - |
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. | - |
dc.language | eng | - |
dc.relation.ispartof | IJCAI International Joint Conference on Artificial Intelligence | - |
dc.title | Spectral embedded clustering | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-78049383727 | - |
dc.identifier.spage | 1181 | - |
dc.identifier.epage | 1186 | - |
dc.identifier.isi | WOS:000283727900187 | - |