Entropy‐based variational Bayes learning framework for data clustering

Fan, Wentao; Bouguila, Nizar; Bourouis, Sami; Laalaoui, Yacine

doi:10.1049/iet-ipr.2018.0043

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…Moreover, the model complexity can be easily solved using for example the marginal likelihood-based technique. Thus, our focus in this paper is to implement an effective Bayesian learning method for SSDMM in order to take into account the complexity of medical data and to overcome the drawbacks of frequentist (deterministic) approaches [ 34 , 35 ]. To the best of our knowledge, such an approach has never been tackled before, especially for the problem of chest x-ray images classification.…”

Section: Motivationsmentioning

confidence: 99%

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021

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Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework.

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Section: Motivationsmentioning

confidence: 99%

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021

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“…For the parameter estimation problem, we focus, here, on the application of variational Bayes with the mean field approximation, which has been shown to be an efficient technique for inferring posterior distributions of mixture models [ 20 , 25 , 26 ]. Indeed, variational Bayes has been proposed as an efficient solution for posteriors approximation with low computational cost, as opposed to other inference approaches such as the MCMC technique [ 8 , 27 ].…”

Section: Variational Bayesian Learning Via Entropy-based Splittingmentioning

confidence: 99%

“…Finally, the solutions of the updated variational posteriors are obtained by optimizing

with respect to each distribution. The resulting solutions are expressed as follows:

where the hyperparameters in the above equations can be fixed in a similar way as in [ 26 ] by testing and experimenting different values depending on the data set to model .…”

Section: Variational Bayesian Learning Via Entropy-based Splittingmentioning

confidence: 99%

Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Bourouis

Pawar

Bouguila

2021

Sensors

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Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.

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“…The third developed learning algorithm is called variational Bayesian inference, which is an efficient alternative to the previous Bayesian learning method [8,33,34]. In this work, we tackle the case of a multi-dimensional problem for variational Bayesian learning to classify biomedical images.…”

Section: Variational Learningmentioning

confidence: 99%

“…In the statistical learning context, we can identify different learning approaches for making inference on parameters in mixture models; in particular, deterministic inference via maximum likelihood (ML) or maximum a posteriori (MAP) and non-deterministic inference via Bayesian inference or variational inference [8,9]. Our work here is motivated by the interesting results obtained with the finite Gamma mixture in the case of data clustering and segmentation.…”

Section: Introductionmentioning

confidence: 99%

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

2020

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The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images’ classification.

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Entropy‐based variational Bayes learning framework for data clustering

Cited by 13 publications

References 33 publications

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

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