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Conference Paper: Exploiting word cluster information for unsupervised feature selection

TitleExploiting word cluster information for unsupervised feature selection
Authors
Issue Date2010
PublisherSpringer.
Citation
11th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2010), Daegu, Korea, 30 August - 2 September 2010. In PRICAI 2010: Trends in Artificial Intelligence: 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30–September 2, 2010: Proceedings, 2010, p. 292-303 How to Cite?
AbstractThis paper presents an approach to integrate word clustering information into the process of unsupervised feature selection. In our scheme, the words in the whole feature space are clustered into groups based on the co-occurrence statistics of words. The resulted word clustering information and the bag-of-word information are combined together to measure the goodness of each word, which is our basic metric for selecting discriminative features. By exploiting word cluster information, we extend three well-known unsupervised feature selection methods and propose three new methods. A series of experiments are performed on three benchmark text data sets (the 20 Newsgroups, Reuters-21578 and CLASSIC3). The experimental results have shown that the new unsupervised feature selection methods can select more discriminative features, and in turn improve the clustering performance. © 2010 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/276877
ISBN
ISSN
2005 Impact Factor: 0.302
2020 SCImago Journal Rankings: 0.249
Series/Report no.Lecture Notes in Computer Science ; 6230

 

DC FieldValueLanguage
dc.contributor.authorWu, Qingyao-
dc.contributor.authorYe, Yunming-
dc.contributor.authorNg, Michael-
dc.contributor.authorSu, Hanjing-
dc.contributor.authorHuang, Joshua-
dc.date.accessioned2019-09-18T08:34:55Z-
dc.date.available2019-09-18T08:34:55Z-
dc.date.issued2010-
dc.identifier.citation11th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2010), Daegu, Korea, 30 August - 2 September 2010. In PRICAI 2010: Trends in Artificial Intelligence: 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30–September 2, 2010: Proceedings, 2010, p. 292-303-
dc.identifier.isbn9783642152450-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276877-
dc.description.abstractThis paper presents an approach to integrate word clustering information into the process of unsupervised feature selection. In our scheme, the words in the whole feature space are clustered into groups based on the co-occurrence statistics of words. The resulted word clustering information and the bag-of-word information are combined together to measure the goodness of each word, which is our basic metric for selecting discriminative features. By exploiting word cluster information, we extend three well-known unsupervised feature selection methods and propose three new methods. A series of experiments are performed on three benchmark text data sets (the 20 Newsgroups, Reuters-21578 and CLASSIC3). The experimental results have shown that the new unsupervised feature selection methods can select more discriminative features, and in turn improve the clustering performance. © 2010 Springer-Verlag Berlin Heidelberg.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofPRICAI 2010: Trends in Artificial Intelligence: 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30–September 2, 2010: Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 6230-
dc.titleExploiting word cluster information for unsupervised feature selection-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-15246-7_28-
dc.identifier.scopuseid_2-s2.0-78049241090-
dc.identifier.spage292-
dc.identifier.epage303-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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