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Latent Mixture Modeling for Clustered Data
[article]
2017
arXiv
pre-print
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We adapt the mixture-of-experts model to the latent distributions, and propose a model in which each cluster-wise density is represented as a mixture of latent experts with cluster-wise mixing proportions ...
This article proposes a mixture modeling approach to estimating cluster-wise conditional distributions in clustered (grouped) data. ...
For modeling independent data, the mixture model with covariates was originally proposed in Jacob et al. (1991) , known as mixture-ofexperts. ...
arXiv:1704.05993v1
fatcat:epexpzt34rbe5osyunznh7n5ze
Mixture of Latent Trait Analyzers for Model-Based Clustering of Categorical Data
[article]
2013
arXiv
pre-print
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However, model-based clustering methods for categorical data are less standard. ...
Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. ...
Acknowledgements
This research was supported by a Science Foundation Ireland Research Frontiers Programme Grant (06/RFP/M040) and Strategic Research Cluster Grant (08/SRC/I1407). ...
arXiv:1301.2167v2
fatcat:l24ja7ast5h7lorx2rbkis2yfq
A Latent Gaussian Mixture Model for Clustering Longitudinal Data
[article]
2018
arXiv
pre-print
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Finite mixture models have become a popular tool for clustering. Amongst other uses, they have been applied for clustering longitudinal data and clustering high-dimensional data. ...
In the latter case, a latent Gaussian mixture model is sometimes used. ...
From
Summary A mixture model for clustering high-dimensional longitudinal data was introduced, which has the ability to account for the longitudinal nature of the data being clustered while also providing ...
arXiv:1804.05133v1
fatcat:or65bynznfh3peoenfty4pn3qe
Mixture of latent trait analyzers for model-based clustering of categorical data
2013
Statistics and computing
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However, model-based clustering methods for categorical data are less standard. ...
Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. ...
Acknowledgements We would like to think the editor, associate editor and reviewers for their insightful comments and suggestions which have greatly improved this paper. ...
doi:10.1007/s11222-013-9389-1
fatcat:ku3aaqdcojfwbfilppuk23j7jm
A mixture of experts latent position cluster model for social network data
2010
Statistical Methodology
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The latent position cluster model extends the latent space model to deal with network data in which clusters of actors exist -actor locations are drawn from a finite mixture model, each component of which ...
Herein, a mixture of experts extension of the latent position cluster model is developed. ...
The mixture of experts latent position cluster model 200 The latent position cluster model can be extended within a mixture of 201 experts framework. ...
doi:10.1016/j.stamet.2010.01.002
fatcat:waklz6jvyzgvjisv3teqt73dzm
Mixture of Experts Modelling with Social Science Applications
[chapter]
2011
Mixtures
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Acknowledgements We would like to thank the participants of the ICMS Workshop on Mixture Estimation and Applications for their insightful feedback on this work. ...
This work has been supported by Science Foundation Ireland Research Frontiers grants (06/RFP/M040 and 09/RFP/MTH2367) and Clique, a Science Foundation Ireland Strategic Research Cluster grant (08/SRC/I1407 ...
a cautionary message in automatically selecting the type of mixture of experts latent position cluster model for analyzing social network data. ...
doi:10.1002/9781119995678.ch5
fatcat:bakxhsfs6bfpzac7v27mzzdo34
Warped Mixtures for Nonparametric Cluster Shapes
[article]
2014
arXiv
pre-print
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The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. ...
To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. ...
Acknowledgements The authors would like to thank Dominique Perrault-Joncas, Carl Edward Rasmussen, and Ryan Prescott Adams for helpful discussions. ...
arXiv:1408.2061v1
fatcat:b5svigdi3jhxjdq7zcgpc6ehs4
Warped Mixtures for Nonparametric Cluster Shapes
[article]
2013
arXiv
pre-print
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The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. ...
To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. ...
Acknowledgements The authors would like to thank Dominique Perrault-Joncas, Carl Edward Rasmussen, and Ryan Prescott Adams for helpful discussions. ...
arXiv:1206.1846v2
fatcat:vgq276gmuzhnjmc55ofbnrko2i
Model-based clustering for multidimensional social networks
[article]
2021
arXiv
pre-print
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We propose the infinite latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. ...
Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low ...
The latter may be thought as a latent class analysis model for network data and it is explicitly designed to model clustering of the nodes. ...
arXiv:2001.05260v2
fatcat:rzrukawln5d65afew5rsxediqu
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data
2021
PLoS Computational Biology
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Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. ...
Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. ...
Acknowledgments AK thanks Florian Buettner for helpful discussions and his inspirational attitude.
Author Contributions Conceptualization: Andreas Kopf, Vincent Fortuin, Manfred Claassen. ...
doi:10.1371/journal.pcbi.1009086
pmid:34191792
pmcid:PMC8277074
fatcat:raq7ki42fng7bd52aa7lv42u6i
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
[article]
2020
arXiv
pre-print
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This specific architecture allows for various modes of the data to be automatically learned by means of the experts.Additionally, we encourage the lower dimensional latent representation of our model to ...
Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model.The model can learn multi-modal distributions of high-dimensional data and ...
In our model, we aim to learn the mixture components in the latent representation to be standard Gaussians z ∼ K k=0 ω k N (µ k , I) (1) where ω k are mixture coefficients, µ k are the means for each mixture ...
arXiv:1910.07763v3
fatcat:dtlquay3lves5gaxpbatcwoa4a
A factor mixture analysis model for multivariate binary data
[article]
2010
arXiv
pre-print
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The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. ...
The aim of the proposed model is twofold: it allows to achieve dimension reduction when the data are dichotomous and, simultaneously, it performs model based clustering in the latent space. ...
For this reason latent class is also referred to as mixture-model clustering (McLachlan and Basford, 1988) or model-based clustering (Banfield and Raftery, 1993; Fraley and Raftery, 2002) . ...
arXiv:1010.2314v1
fatcat:rwhyb7tyffdqlpls7fcba5tlai
The Impact of Test and Sample Characteristics on Model Selection and Classification Accuracy in the Multilevel Mixture IRT Model
2020
Frontiers in Psychology
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Mixture extensions of IRT models have been proposed to account for latent heterogeneous populations, but these models are not designed to handle multilevel data structures. ...
As yet, no studies appear to have been reported examining these issues for multilevel extensions of mixture IRT models. ...
Mixture IRT models combine a latent class model and an IRT model in a single model. ...
doi:10.3389/fpsyg.2020.00197
pmid:32116973
pmcid:PMC7033749
fatcat:2kbaopuzm5hp7hxcxdzxnq372q
A tutorial on Bayesian nonparametric models
2012
Journal of Mathematical Psychology
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This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. ...
In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. ...
Mixture models and clustering In a mixture model, each observed data point is assumed to belong to a cluster. ...
doi:10.1016/j.jmp.2011.08.004
fatcat:allxc5i5qbcnvazdss67z7qw3y
Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization
2016
PLoS ONE
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Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios ...
Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine ...
The MSE of the mixture of regression model decreases as the number of latent clusters is increased. ...
doi:10.1371/journal.pone.0162812
pmid:27636203
pmcid:PMC5026362
fatcat:m3w4a5xhbjhjlhfzofwih3glxm
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