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IJCNN
2007
IEEE

Iterative Feature Selection in Gaussian Mixture Clustering with Automatic Model Selection

10 years 4 months ago
Iterative Feature Selection in Gaussian Mixture Clustering with Automatic Model Selection
— This paper proposes an algorithm to deal with the feature selection in Gaussian mixture clustering by an iterative way: the algorithm iterates between the clustering and the unsupervised feature selection. First, we propose a quantitative measurement of the feature relevance with respect to the clustering. Then, we design the corresponding feature selection scheme and integrate it into the Rival Penalized EM (RPEM) clustering algorithm (Cheung 2005) that is able to determine the number of clusters automatically. Subsequently, the clustering can be performed in an appropriate feature subset by gradually eliminating the irrelevant features with automatic model selection. Compared to the existing methods, the numerical experiments have shown the efficacy of the proposed algorithm on the synthetic and real world data.
Hong Zeng, Yiu-ming Cheung
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IJCNN
Authors Hong Zeng, Yiu-ming Cheung
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