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Updated: Jun 22, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering
Sabri Boutemedjet1, Nizar Bouguila, Djemel Ziou
1Université de Sherbrooke, Sherbrooke, QC, Canada. sabri.boutemedjet@usherbrooke.ca
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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