caa a22 4500 605468273 CHVBK 20210128100315.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1242-8 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1242-8 A hierarchical nonparametric Bayesian approach for medical images and gene expressions classification [Elektronische Daten] [Tarek Elguebaly, Nizar Bouguila] Lately, the enormous generation of databases in almost every aspect of life has created a great demand for new powerful tools for turning data into useful information. Therefore, researchers were encouraged to explore and develop new machine learning ideas and methods. Mixture models are one of the machine learning techniques receiving considerable attention due to their ability to handle efficiently and effectively multidimensional data. In this paper, we represent a solution for two challenging issues: modeling non-Gaussian data and determining the set of relevant features in the data. The problem of modeling non-Gaussian data largely present in several computer vision, image processing, medical, and Bioinformatics applications is accomplished by the development of a generative infinite Gamma mixture model. The Gamma is chosen for its ability to handle long-tailed distributions, which allows it to have a good approximation to data with outliers. The proposed model, which can be viewed as a Dirichlet process mixture of Gamma distributions, takes into account the feature selection problem by determining a set of relevant features for each data cluster which provides better interpretability and generalization capabilities. We propose then an efficient algorithm to learn this infinite model's parameters by estimating all its posterior quantities of interest using Markov Chain Monte Carlo (MCMC) simulations. Thus, our algorithm is able to perform model selection, parameter learning, and feature selection simultaneously in a single step for the Gamma Mixture model. Furthermore, we show how the model can be used, while comparing it with other popular models in the literature, in two challenging applications namely medical images and gene expressions classification. Springer-Verlag Berlin Heidelberg, 2014 Mixture models nationallicence Gamma distribution nationallicence Feature selection nationallicence Nonparametric Bayes nationallicence MCMC nationallicence Gibbs sampling nationallicence Mammography nationallicence Gene expression nationallicence Elguebaly Tarek Electrical and Computer Engineering Department (ECE), Concordia University, H3G 1T7, Montreal, QC, Canada aut Bouguila Nizar Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1T7, Montreal, QC, Canada aut Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 189-204 1432-7643 19:1<189 2015 19 500 https://doi.org/10.1007/s00500-014-1242-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1242-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Elguebaly Tarek Electrical and Computer Engineering Department (ECE), Concordia University, H3G 1T7, Montreal, QC, Canada aut NATIONALLICENCE 700 1- Bouguila Nizar Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1T7, Montreal, QC, Canada aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 189-204 1432-7643 19:1<189 2015 19 500