Gaussian Mixture Models (GMMs)
Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part I. Theory and Scheme
... Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts ABSTRACT This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent ...
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Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications
... with Gaussian Mixture Models in a filtering context; the latter further exemplifies its ability to efficiently handle state vectors of non-trivial dimensionality and dynamics with jets and ...of ...
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Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
... on Gaussian Mixture Models (GMM) have several interesting properties that make them suit- able for feature selection in the context of large amount of ...
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A Wasserstein-type distance in the space of Gaussian Mixture Models
... distance, Gaussian mixture model, multi-marginal optimal trans- port, barycenter, image processing applications AMS subject ...Nowadays, Gaussian Mixture Models (GMM) have become ...
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Classification of Outdoor 3D Lidar Data Based on Unsupervised Gaussian Mixture Models
... Unsupervised Gaussian Mixture Models Artur Maligo, Simon Lacroix Abstract—3D point clouds acquired with lidars are an im- portant source of data for the classification of outdoor envi- ronments by ...
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Fast training of Large Margin diagonal Gaussian mixture models for speaker identification
... Recently a new discriminative approach for multiway classification has been proposed, the Large Margin Gaussian mixture models (LM-GMM) [4]. The latter have the same advantage as SVM in terms of the ...
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On the Variational Posterior of Dirichlet Process Deep Latent Gaussian Mixture Models
... 6. Conclusion In this paper, we have presented a variational inference method for Dirichlet Process Deep Latent Gaussian Mixture Models. Our approach combines classical variational infer- ence and ...
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Sound event detection in remote health care - Small learning datasets and over constrained Gaussian Mixture Models
... a Mixture of Gaussians. How- ever, Gaussian Mixture Models provided by the Parzen method are intrinsically regularized, for kernel centers cannot move (structural regularization - category IV) ...
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Operational Modal Analysis in Frequency Domain using Gaussian Mixture Models
... of second order differential system. This assumption fails for non-linear systems and for cases where modal frequencies are very close. In the following section we propose to use Gaussian Mixture ...
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Operational Modal Analysis in Frequency Domain using Gaussian Mixture Models
... of second order differential system. This assumption fails for non-linear systems and for cases where modal frequencies are very close. In the following section we propose to use Gaussian Mixture ...
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An exact line search scheme to accelerate the EM algorithm Application to Gaussian mixture models identification
... This paper tackles the slowness issue of the well-known Expectation-Maximization (EM) algorithm in the context of Gaussian Mixture Models. To cope with this slowness problem, an Exact Line Search ...
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PCA Reduced Gaussian Mixture Models with Applications in Superresolution
... However, any of these models requires the estimation of the parameters of a GMM using the patches of the images as data points. For this, the maximum likelihood (ML) estimator is used, which corresponds to ...
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Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
... rate is computed as the mean test error over the n cv subsets of S. 2) Similarity between distributions: The similarity between two distributions can be quantified using divergence mea- sures [37]. Contrary to measures ...
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Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data
... KEYWORDS: model-based classification, high-dimensional gaussian model, generative model, vibrational spectroscopy. 1 Introduction Supervised classification, which aims at attributing unlabeled samples to known ...
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Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations
... Adopting techniques prevalent in Machine Learning and Pattern Recognition, and building on the foundations of classical assimilation schemes, we introduce the GMM-DO [r] ...
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Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach
... for Gaussian mixtures. As fitting a Gaussian mixture on a data set is equivalent to building a cluster structure, we measured the posterior couple error (this measure penalizes cluster structures ...
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Organizing Gaussian mixture models into a tree for scaling up speaker retrieval
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ...
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Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach
... class models may be investigated ...parent models from similar leave models in a tree [11], or merging mod- els describing the same class, ...
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Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering
... by Gaussian mixture models ...probabilistic models where all the data points are generated from a mixture of a finite number r of Gaussian ...
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A non asymptotic penalized criterion for Gaussian mixture model selection
... of models. For Gaussian mixture models, we can only fol- low the second alternative because of the non linear behavior of the logarithm function on Gaussian mixture ...
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