Discrete data clustering using finite mixture models

Bouguila, Nizar; ElGuebaly, Walid

doi:10.1016/j.patcog.2008.06.022

Cited by 47 publications

(17 citation statements)

References 29 publications

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“…Two main approaches are the maximum likelihood (ML) and the maximum a posteriori (MAP). However, as noticed in [37], in our case the ML approach gives poor estimates and may suffer from the zero counts, since it is based only on the frequencies. Thus, following [13], we adopt a MAP estimation:…”

Section: B Model Learningmentioning

confidence: 75%

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Bouguila

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

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This paper presents an unsupervised statistical model for simultaneous clustering, feature selection and outlier rejection in the case of count data. The proposed model is based on a finite discrete mixture to which a uniform component is added to ensure robustness to outliers and noise. The consideration of a finite mixture model is justified by its flexibility, its solid grounding in the theory of statistics and its competitive results. We derive a complete maximum a posteriori learning approach that does not require a priori knowledge about the number of outliers and the number of clusters. A rigorous expectation maximization (EM) algorithm, based on the formulation of a maximum a posteriori (MAP) estimation, is also provided. We report experimental results of applying our model to the challenging problems of visual scenes categorization and texture discrimination.

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Section: B Model Learningmentioning

confidence: 75%

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Bouguila

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

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“…For example, some work has been done using symmetric component densities that parameterize concentration (tail weight), e.g., the t distribution , Andrews & McNicholas 2011, Lin, McNicholas & Hsiu 2014) and the power exponential distribution (Dang, Browne & McNicholas 2015). There has also been work on mixtures for discrete data (e.g., Karlis & Meligkotsidou 2007, Bouguila & ElGuebaly 2009) as well as several examples of mixtures of skewed distributions such as the NIG distribution (Karlis & Santourian 2009, Subedi & McNicholas 2014, the skew-t distribution (Lin 2010, Vrbik & McNicholas 2012, Lee & McLachlan 2014, 2016, the shifted asymmetric Laplace distribution (Morris & McNicholas 2013, Franczak, Browne & McNicholas 2014, the variance-gamma distribution , the generalized hyperbolic distribution , and others (e.g., Elguebaly & Bouguila 2015, Franczak, Tortora, Browne & McNicholas 2015.…”

Section: Model-based Clustering and Mixture Modelsmentioning

confidence: 99%

A matrix variate skew‐t distribution

Gallaugher

McNicholas

2017

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Clustering is the process of finding underlying group structures in data. Although mixture model-based clustering is firmly established in the multivariate case, there is a relative paucity of work on matrix variate distributions and none for clustering with mixtures of skewed matrix variate distributions. Four finite mixtures of skewed matrix variate distributions are considered. Parameter estimation is carried out using an expectation-conditional maximization algorithm, and both simulated and real data are used for illustration.

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“…This limitation is widely recognized as a challenging problem which justifies several researches that have been proposed to integrate such spatial information (Bouguila & ElGuebaly, 2009;Huang, Kumar, Mitra, Zhu, & Zabih, 1999). Here we present an alternative approach based on the color correlogram method (Huang et al, 1999).…”

Section: Color Spatial Information Modeling For Content-based Image Cmentioning

confidence: 99%