A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
Sabri Boutemedjet, Nizar Bouguila, Djemel Ziou · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2009
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.