Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications

Manouchehri, Narges; Bouguila, Nizar; Fan, Wentao

doi:10.1007/s10044-021-01023-6

Cited by 10 publications

(2 citation statements)

References 68 publications

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“…Beyond tracking, HDP finds applications in natural language processing, topic modeling, image segmentation, and clustering tasks. In these contexts, HDP offers a flexible and scalable framework for capturing latent structures and patterns in data, making it a versatile tool for various machine learning and statistical applications [26,62,78,15,80,35,36,67].…”

Section: Hierarchical Dirichlet Process Modelingmentioning

confidence: 99%

METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics

Moraffah

Richmond

Moraffah

et al. 2020

2020 54th Asilomar Conference on Signals, Systems, and Computers

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Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets. This survey intends to delve into the significance of Bayesian nonparametrics, particularly in addressing complex challenges across various domains such as statistics, computer science, and electrical engineering. By elucidating the basic properties and theoretical foundations of these nonparametric models, this survey aims to provide a comprehensive understanding of Bayesian nonparametrics and their relevance in addressing complex problems, particularly in the domain of multi-object tracking. Through this exploration, we uncover the versatility and efficacy of Bayesian nonparametric methodologies, paving the way for innovative solutions to intricate challenges across diverse disciplines.

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Section: Hierarchical Dirichlet Process Modelingmentioning

confidence: 99%

METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics

Moraffah

Richmond

Moraffah

et al. 2020

2020 54th Asilomar Conference on Signals, Systems, and Computers

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“…VI is an approximation technique, more accurate compared to ML and faster than fully Bayesian inference [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 ]. Moreover, compared to a deterministic method, such as maximum likelihood, it does not suffer from convergence to a local maximum and over-fitting.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with an HMM-Based Generative Model

Manouchehri

Bouguila

2023

Sensors

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Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

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A Flexible and Hybrid Feature Mapping Technique Based on multivariate Beta Distribution Kernel Applied to Medical Applications

Manouchehri

Bouguila

2022

2022 IEEE International Conference on Industrial Technology (ICIT)

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Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications

Cited by 10 publications

References 68 publications

METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics

METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics

Human Activity Recognition with an HMM-Based Generative Model

A Flexible and Hybrid Feature Mapping Technique Based on multivariate Beta Distribution Kernel Applied to Medical Applications

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