A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering |
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Authors: | Boutemedjet Sabri Bouguila Nizar Ziou Djemel |
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Affiliation: | Université de Sherbrooke, Sherbrooke; |
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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. |
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