A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
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.
feature selection mixture models unsupervised learning dimensionality reduction generalized dirichlet mixture em mml information theory object image categorization