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Bayesian approaches to Gaussian mixture modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998 A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an "optimal" number of components in the model and so partition data sets.
Roberts, S +3 more
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Quantum-like Gaussian mixture model [PDF]
Soft Computing, 2021 Abstract A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The decision boundary of a quantum-like mixture Gaussian corresponds as well to the separation of probabilities for the switching Kalman filter. The quantum-
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Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
Algorithms, 2023 Gaussian mixture modeling is a generative probabilistic model that assumes that the observed data are generated from a mixture of multiple Gaussian distributions. This mixture model provides a flexible approach to model complex distributions that may not
Luca Scrucca
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Gaussian mixture model for extreme wind turbulence estimation [PDF]
Wind Energy Science, 2022 Uncertainty quantification is necessary in wind turbine design due to the random nature of the environmental inputs, through which the uncertainty of structural loads and response under specific situations can be quantified. Specifically, wind turbulence
X. Zhang, A. Natarajan
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Optimal Transport for Gaussian Mixture Models [PDF]
IEEE Access, 2019 We present an optimal mass transport framework on the space of Gaussian mixture models, which are widely used in statistical inference. Our method leads to a natural way to compare, interpolate and average Gaussian mixture models. Basically, we study such models on a certain submanifold of probability densities with certain structure. Different aspects
Yongxin Chen +2 more
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Anchored Bayesian Gaussian mixture models
Electronic Journal of Statistics, 2020 Finite mixtures are a flexible modeling tool for irregularly shaped densities and samples from heterogeneous populations. When modeling with mixtures using an exchangeable prior on the component features, the component labels are arbitrary and are indistinguishable in posterior analysis.
Kunkel, Deborah, Peruggia, Mario
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Gaussian mixture model‐based contrast enhancement [PDF]
IET Image Processing, 2015 In this study, a method for enhancing low‐contrast images is proposed. This method, called Gaussian mixture model‐based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian‐shaped histogram, it ...
Abdoli, Mohsen +3 more
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The learning method of robot teaching sewing motion
Xi'an Gongcheng Daxue xuebao, 2022 In order to realize the robot′s learning of teaching sewing motion, a robot motion learning method based on Gaussian Mixture Model (GMM) -Gaussian Mixture Regression (GMR) was proposed.
WANG Haoyi, WANG Xiaohua, WANG Wenjie
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Human action recognition based on mixed gaussian hidden markov model [PDF]
MATEC Web of Conferences, 2021 Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field.
Xu Jiawei, Luo Qian
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Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas [PDF]
, 2013 <br>This paper presents a finite mixture of multivariate betas as a new model-based clustering method tailored to applications where the feature space is constrained to the unit hypercube.
Dean, Nema, Nugent, Rebecca
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