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23 papers · ranked by Valyu relevance

01
PPubMed5 Apr 2022
Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction
Vassilis Alimisis, Georgios Gennis, Konstantinos Touloupas, Christos Dimas + 3 more
This paper presents a new analog front-end classification system that serves as a wake-up engine for digital back-ends, targeting embedded devices for epileptic seizure prediction. Predicting epileptic seizures is of major importance for the patient’s quality of life as they can lead to paralyzation or even prove…
90relevance
02
CchemRxiv25 Apr 2025
Generative machine learning approaches to optimization
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root ﬁnding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
90relevance
03
AarXiv2023
An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network
Weiguo Lu, Xuan Wu, Deng Ding, Gangnan Yuan
We propose an Gaussian Mixture Model(GMM) learning algorithm, based on our previous work of GMM expansion idea. The new algorithm brings more robustness and simplicity than classic Expectation Maximization (EM) algorithm. It also improves the accuracy and only take 1 iteration for learning. We theoretically proof that…
89relevance
04
PPubMed13 Apr 2023
A new iterative initialization of EM algorithm for Gaussian mixture models A new iterative initialization of EM algorithm for Gaussian mixture models
Jie You, Zhaoxuan Li, Junli Du, Praveen Kumar Donta
For d-dimensional random variable X with n samples, the probability distribution of a finite Gaussian mixture model can be expressed by a weighted sum of K components : P ( X | Θ ) = ∑ m = 1 K α m p m ( X | θ m ) , (1) where α*m is m-th mixing proportion, which must satisfy α**m > 0, m = 1, …, K and ∑ m = 1 K α m = 1.…
88relevance
05
AarXiv2017
Variable selection for clustering with Gaussian mixture models: state of the art
Abdelghafour Talibi, Boujemâa Achchab, Rafik Lasri
The mixture models have become widely used in clustering, given its probabilistic framework in which its based, however, for modern databases that are characterized by their large size, these models behave disappointingly in setting out the model, making essential the selection of relevant variables for this type of…
88relevance
06
PPubMed2 Sep 2019
Kernel Analysis Based on Dirichlet Processes Mixture Models
Jinkai Tian, Peifeng Yan, Da Huang
Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels. However, the number of components in a GMM is fixed. Thus, this model suffers from…
87relevance
07
BbioRxiv23 Dec 2019
MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Genomics Data
Zachary R. McCaw, Hanna Julienne, Hugues Aschard
Although missing data are prevalent in genetic and genomics data sets, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased…
86relevance
08
BbioRxiv24 Jul 2016
Transformation and model choice for RNA-seq co-expression analysis
Andrea Rau, Cathy Maugis-Rabusseau
Although a large number of clustering algorithms have been proposed to identify groups of co-expressed genes from microarray data, the question of if and how such methods may be applied to RNA-seq data remains unaddressed. In this work, we investigate the use of data transformations in conjunction with Gaussian mixture…
86relevance
09
BbioRxiv15 Dec 2019
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data
Siva Rajesh Kasa, Vaibhav Rajan
We study two practically important cases of model based clustering using Gaussian Mixture Models: (1) when there is misspecification and (2) on high dimensional data, in the light of recent advances in Gradient Descent (GD) based optimization using Automatic Differentiation (AD). Our simulation studies show that EM has…
84relevance
10
CchemRxiv8 Apr 2025
Solving an inverse problem with generative models
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
83relevance
11
AarXiv2017
Mixture model fitting using conditional models and modal Gibbs sampling
Virgilio Gómez‐Rubio
Mixture models are a convenient way of modeling data using a convex combination of different parametric distributions. In this paper, we present a novel approach to fitting mixture models based on estimating first the posterior distribution of the auxiliary variables that assign each observation to a group in the…
82relevance
12
AarXiv2014
Model-based clustering of Gaussian copulas for mixed data
Matthieu Marbac, Christophe Biernacki, Vincent Vandewalle
Clustering task of mixed data is a challenging problem. In a probabilistic framework, the main difficulty is due to a shortage of conventional distributions for such data. In this paper, we propose to achieve the mixed data clustering with a Gaussian copula mixture model, since copulas, and in particular the Gaussian…
81relevance
13
AarXiv10 Jul 2024
Variational Bayes for Mixture of Gaussian Structural Equation Models
Khue‐Dung Dang, Luca Maestrini, Francis K. C. Hui
Structural equation models (SEMs) are commonly used to study the structural relationship between observed variables and latent constructs. Recently, Bayesian fitting procedures for SEMs have received more attention thanks to their potential to facilitate the adoption of more flexible model structures, and variational…
81relevance
14
PPubMed5 Nov 2021
Erratum: Jamšek et al. Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons. <i> Sensors </i> 2020, <i> 20 </i> , 2705
Marko Jamšek, Tadej Petrič, Jan Babič
There was an error in the original article [1]. The variable used for the superscript in the denominator of the equation, denoting the dimensionality of the model, was incorrectly written as K and thus conflicted with the variable denoting the number of Gaussian mixtures. A correction has therefore been made to Section…
76relevance
15
AarXiv21 Dec 2016
Strong Gaussian approximation of the mixture Rasch model
Friedrich Liese, Alexander Meister, Johanna Kappus
We consider the famous Rasch model, which is applied to psychometric surveys when n persons under test answer m questions. The score is given by a realization of a random binary n × m-matrix. Its (j, k)th component indicates whether or not the answer of the jth person to the kth question is correct. In the mixture…
76relevance
16
PPubMed28 Dec 2021
Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures
Sami Bourouis, Yogesh Pawar, Nizar Bouguila, Gemine Vivone
Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector…
75relevance
17
PPubMed12 Jan 2017
Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models Bayesian Criteria for Number of Latent Classes
Kazem Nasserinejad, Joost van Rosmalen, Wim de Kort, Emmanuel Lesaffre + 1 more
'Emmanuel Lesaffre' 'Ulrich S Tran'] Identifying the number of classes in Bayesian finite mixture models is a challenging problem. Several criteria have been proposed, such as adaptations of the deviance information criterion, marginal likelihoods, Bayes factors, and reversible jump MCMC techniques. It was recently…
74relevance
18
PPubMed10 Nov 2023
Local Bayesian Dirichlet mixing of imperfect models
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To…
74relevance
19
BbioRxiv27 May 2024
Bayesian model-based method for clustering gene expression time series with multiple replicates
Elio Nushi, François P. Douillard, Katja Selby, Miia Lindström + 1 more
In this study, we introduce a Bayesian model-based method for clustering transcriptomics time series data with multiple replicates. This technique is based on sampling Gaussian processes (GPs) within an infinite mixture model from a Dirichlet process (DP). Our method uses multiple GP models to accommodate for multiple…
72relevance
20
AarXiv2017
Embarrassingly Parallel Inference for Gaussian Processes
Michael Minyi Zhang, Sinead A. Williamson
Training Gaussian process-based models typically involves an O(N3 ) computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix inversion problem cannot adequately model all types of latent functions, and are often not parallelizable. However, judicious choice of model…
71relevance
21
CchemRxiv16 May 2025
A fast and lightweight implementation of the Gaussian puff model for near-field atmospheric transport of trace gasses
Authors not listed
Atmospheric dispersion models are a key component for characterizing methane emissions on oil and gas sites. While some model implementations with varying degrees of complexity are available, existing regulatory-grade dispersion models are cumbersome to apply within an inversion framework on a routine operational level…
62relevance
22
CchemRxiv10 Dec 2025
Bayesian Calibration of HEOS Mixing Models and Impact on Thermodynamic Cycles
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
62relevance
23
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
50relevance

Results for Gaussian mixture model

Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction

Generative machine learning approaches to optimization

An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network

A new iterative initialization of EM algorithm for Gaussian mixture models A new iterative initialization of EM algorithm for Gaussian mixture models

Variable selection for clustering with Gaussian mixture models: state of the art

Kernel Analysis Based on Dirichlet Processes Mixture Models

MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Genomics Data

Transformation and model choice for RNA-seq co-expression analysis

Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data

Solving an inverse problem with generative models

Mixture model fitting using conditional models and modal Gibbs sampling

Model-based clustering of Gaussian copulas for mixed data

Variational Bayes for Mixture of Gaussian Structural Equation Models

Erratum: Jamšek et al. Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons. <i> Sensors </i> 2020, <i> 20 </i> , 2705

Strong Gaussian approximation of the mixture Rasch model

Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models Bayesian Criteria for Number of Latent Classes

Local Bayesian Dirichlet mixing of imperfect models

Bayesian model-based method for clustering gene expression time series with multiple replicates

Embarrassingly Parallel Inference for Gaussian Processes

A fast and lightweight implementation of the Gaussian puff model for near-field atmospheric transport of trace gasses

Bayesian Calibration of HEOS Mixing Models and Impact on Thermodynamic Cycles

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction

Generative machine learning approaches to optimization

An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network

A new iterative initialization of EM algorithm for Gaussian mixture models A new iterative initialization of EM algorithm for Gaussian mixture models

Variable selection for clustering with Gaussian mixture models: state of the art

Kernel Analysis Based on Dirichlet Processes Mixture Models

MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Genomics Data

Transformation and model choice for RNA-seq co-expression analysis

Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data

Solving an inverse problem with generative models

Mixture model fitting using conditional models and modal Gibbs sampling

Model-based clustering of Gaussian copulas for mixed data

Variational Bayes for Mixture of Gaussian Structural Equation Models

Erratum: Jamšek et al. Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons. <i> Sensors </i> 2020, <i> 20 </i> , 2705

Strong Gaussian approximation of the mixture Rasch model

Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models Bayesian Criteria for Number of Latent Classes

Local Bayesian Dirichlet mixing of imperfect models

Bayesian model-based method for clustering gene expression time series with multiple replicates

Embarrassingly Parallel Inference for Gaussian Processes

A fast and lightweight implementation of the Gaussian puff model for near-field atmospheric transport of trace gasses

Bayesian Calibration of HEOS Mixing Models and Impact on Thermodynamic Cycles

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics