A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering.
basic_science · Level V
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- Record sourced from PubMed, PMID 19542577.
- Also identified by DOI 10.1109/TPAMI.2008.155.
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Abstract
This paper presents an unsupervised approach for feature selection and extraction in mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture model that is able to extract independent and non-Gaussian features without loss of accuracy. The proposed model is learned using the Expectation-Maximization algorithm by minimizing the message length of the data set. Experimental results show the merits of the proposed methodology in the categorization of object images.