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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Bibliographic Details
Published in:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 1; pp. 9 - 15
Main Authors: Cheung, Yiu-Ming, Jia, Hong
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
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
ISBN:9781467360579, 1467360570
Online Access:Get full text
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Summary: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.
ISBN:9781467360579
1467360570
DOI:10.1109/WI-IAT.2012.259