Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition

Najar, Fatma; Bourouis, Sami; Bouguila, Nizar; Belghith, Safya

doi:10.1007/s11042-018-7116-9

Cited by 44 publications

(27 citation statements)

References 32 publications

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“…In deterministic (called also frequentist) approaches, all the model's parameters are considered as fixed and unknown, and inference is driven by the likelihood of the data [28]. For this reason, the well known technique named maximum likelihood (ML) is generally applied in this case, which can be formalized as the following optimization problem.…”

Section: Deterministic Learningmentioning

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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Section: Deterministic Learningmentioning

confidence: 99%

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

2020

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“…It was developed as an alternative solution based on a statistical learning technique for both data regression and classification. It has been effectively employed for several related applications [42][43][44][45]. SVM can be used for either supervised or unsupervised learning.…”

Section: Linear and Non-linear Support Vector Machines(svm)mentioning

confidence: 99%

Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

et al. 2020

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Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis. Then, an effective hybrid solution to further improve the expected results is proposed here. It allows to consider the benefits of the cooperation of different complementary approaches such as statistical-based, variational-based and atlas-based techniques and to reduce their drawbacks. In particular, a pipeline framework that involves different steps such as a preprocessing step, a classification step and a refinement step with variational-based method is developed to identify accurately pathological regions in biomedical images. The preprocessing step has the role to remove noise and improve the quality of the images. Then the classification is based on both symmetry axis detection step and non linear learning with SVM algorithm. Finally, a level set-based model is performed to refine the boundary detection of the region of interest. In this work we will show that an accurate initialization step could enhance final performances. Some obtained results are exposed which are related to the challenging application of brain tumor segmentation.

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“…Machine learning-based approaches (Bayesian approaches, Neural Networks, Statistical mixture models, SVM, Hidden Markov model, genetic algorithms, etc.) [33], [34], [35], [36], [37], [38], [39], [1], [2] have been proposed as a powerful techniques to solve several issues related to IDS, alert classification and intrusion detection problems. In particular, they are considered as effective tools for complex data modeling able to represent alerts in a compact form, to filter and to reduce the huge quantity of false alerts and to identify abnormal activities.…”

Section: Machine Learning (Ml) Perspectivesmentioning

confidence: 99%

“…These distributions are then used to model the observation vectors. There are some researchers who have tried to apply EM-algorithm for alert clustering such as in [51], [52], [38], [53]. From a technical point of view, the distribution of the generated alerts can be approximated according to multivariate probability distribution given that an attack instance is considered as a random process.…”

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confidence: 99%

Alerts Clustering for Intrusion Detection Systems: Overview and Machine Learning Perspectives

Alhakami¹

2019

IJACSA

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The tremendous amount of the security alerts due to the high-speed alert generation of high-speed networks make the management of intrusion detection computationally expensive. Evidently, the high-level rate of wrong alerts disproves the Intrusion Detection Systems (IDS) performances and decrease its capability to prevent cyber-attacks which lead to tedious alert analysis task. Thus, it is important to develop new tools to understand intrusion data and to represent them in a compact forms using, for example, an alert clustering process. This hot topic of research is studied here and an understandable taxonomy followed by a deep survey of main published works related to intrusion alert management is presented in this paper. The second part of this work exposes different useful steps for designing a unified IDS system on the basis of machine learning techniques which are considered one of the most powerful tools for solving certain problems related to alert management and outlier detection.

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Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition

Cited by 44 publications

References 32 publications

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

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

Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Alerts Clustering for Intrusion Detection Systems: Overview and Machine Learning Perspectives

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