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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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Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model
, 2020 The growing heterogeneity due to diverse Cloud workloads such as Big Data, IoT and Business Data analytics, requires precise characterization to design a successful capacity plan and maintain the competitiveness of Cloud service providers.
Eva Patel, D. S. Kushwaha
semanticscholar +1 more source
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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Superpixel Segmentation Using Gaussian Mixture Model [PDF]
IEEE Transactions on Image Processing, 2018 Superpixel segmentation algorithms are to partition an image into perceptually coherence atomic regions by assigning every pixel a superpixel label. Those algorithms have been wildly used as a preprocessing step in computer vision works, as they can enormously reduce the number of entries of subsequent algorithms.
Zhihua Ban, Jianguo Liu, Li Cao
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Clustering Analysis in the Wireless Propagation Channel with a Variational Gaussian Mixture Model
IEEE Transactions on Big Data, 2020 In this paper, the Gaussian mixture model (GMM) is introduced to implement channel multipath clustering. The GMM incorporates the covariance structure and the mean information of the channel multipaths, thus it can effectively reveal the similarity of ...
Yupeng Li +3 more
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Fitting a Gaussian Mixture Model Through the Gini Index
International Journal of Applied Mathematics and Computer Science, 2021 A linear combination of Gaussian components is known as a Gaussian mixture model. It is widely used in data mining and pattern recognition. In this paper, we propose a method to estimate the parameters of the density function given by a Gaussian mixture ...
López-Lobato Adriana Laura +1 more
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