Unsupervised Feature Selection with Feature Clustering
As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that...
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| Vydáno v: | 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Ročník 1; s. 9 - 15 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.12.2012
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| Témata: | |
| ISBN: | 9781467360579, 1467360570 |
| On-line přístup: | Získat plný text |
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| Abstract | As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that the features in the same cluster contain similar structural information of the given instances. Subsequently, since the obtained feature subset consists of features from variant clusters, the similarity between selected features will be low. This allows us to reserve the most data structural information with the minimum number of features. Experimental results on different benchmark data sets demonstrate the superiority of the proposed method. |
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| AbstractList | As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that the features in the same cluster contain similar structural information of the given instances. Subsequently, since the obtained feature subset consists of features from variant clusters, the similarity between selected features will be low. This allows us to reserve the most data structural information with the minimum number of features. Experimental results on different benchmark data sets demonstrate the superiority of the proposed method. |
| Author | Jia, Hong Cheung, Yiu-Ming |
| Author_xml | – sequence: 1 givenname: Yiu-Ming surname: Cheung fullname: Cheung, Yiu-Ming email: ymc@comp.hkbu.edu.hk organization: Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong, China – sequence: 2 givenname: Hong surname: Jia fullname: Jia, Hong email: hjia@comp.hkbu.edu.hk organization: Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong, China |
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| Snippet | As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a... |
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| SubjectTerms | Clustering algorithms Computational modeling Feature Clustering Feature extraction Feature Redundancy Filters High dimensional data Laplace equations Number of Features Partitioning algorithms Principal component analysis Redundancy Unsupervised Feature Selection Vectors |
| Title | Unsupervised Feature Selection with Feature Clustering |
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