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Variable selection in model-based clustering: A general variable role modeling

C. Maugis, G. Celeux, M.-L. Martin-Magniette
2009 Computational Statistics & Data Analysis  
A model selection criterion and a variable selection algorithm are derived for this new variable role modeling.  ...  The currently available variable selection procedures in model-based clustering assume that the irrelevant clustering variables are all independent or are all linked with the relevant clustering variables  ...  Then a lower bound of ln[f indep (x W |γ, τ )] is ln[f indep (x W |γ, τ )] ≥ − W 2 ln[2πs M ] − 2( x 2 + η 2 ) s m .Variable selection in model-based clustering: A general variable role modeling 19 and  ... 
doi:10.1016/j.csda.2009.04.013 fatcat:nvxurn4mqrberdbpmvtd3mqshm

Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering [article]

Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau, Adrian E. Raftery
2013 arXiv   pre-print
We compare two major approaches to variable selection in clustering: model selection and regularization.  ...  Based on previous results, we select the method of Maugis et al. (2009b), which modified the method of Raftery and Dean (2006), as a current state of the art model selection method.  ...  As a result, there has been considerable interest in variable selection for model-based clustering. Two of the most used general approaches have been model selection and regularization.  ... 
arXiv:1307.7860v1 fatcat:dmnu7wgf75br3omq6hhqeejjfu

clustvarsel: A Package Implementing Variable Selection for Model-based Clustering in R [article]

Luca Scrucca, Adrian E. Raftery
2014 arXiv   pre-print
Variable or feature selection is of particular importance in situations where only a subset of the available variables provide clustering information.  ...  Finite mixture modelling provides a framework for cluster analysis based on parsimonious Gaussian mixture models.  ...  Raftery and Dean (2006) discussed the problem of variable selection for model-based clustering by recasting the problem as a model selection procedure.  ... 
arXiv:1411.0606v1 fatcat:eqkiek3t65fcvjch6rydb5irli

Variable selection in model-based clustering and discriminant analysis with a regularization approach [article]

Gilles Celeux, Cathy Maugis-Rabusseau, Mohammed Sedki
2017 arXiv   pre-print
Relevant methods of variable selection have been proposed in model-based clustering and classification.  ...  In this paper, an alternative regularization approach of variable selection is proposed for model-based clustering and classification.  ...  After a series of papers on variable selection in model-based clustering (Law et al, 2004; Tadesse et al, 2005; Raftery and Dean, 2006; Maugis et al, 2009a) , Maugis et al (2009b) proposed a general  ... 
arXiv:1705.00946v1 fatcat:k7mv4p6zmzderk27cp4atduupq

Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering

Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau, Adrian E Raftery
2014 Journal de la SFdS  
We compare two major approaches to variable selection in clustering: model selection and regularization.  ...  But the model selection approach is not available in a very high dimension context.  ...  As a result, there has been considerable interest in variable selection for model-based clustering. Two of the most used general approaches have been model selection and regularization.  ... 
pmid:25279246 pmcid:PMC4178956 fatcat:zdlt3o5cebg37fg4zd6vwepzxm

clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R

Luca Scrucca, Adrian E. Raftery
2018 Journal of Statistical Software  
Variable or feature selection is of particular importance in situations where only a subset of the available variables provide clustering information.  ...  Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mixture models.  ...  Raftery & Dean (2006) discussed the problem of variable selection for model-based clustering by recasting the problem as a model selection procedure.  ... 
doi:10.18637/jss.v084.i01 pmid:30450020 pmcid:PMC6238955 fatcat:2v6ypaetgvdfxjxwobsp2iqsxu

Variable selection in model-based clustering and discriminant analysis with a regularization approach

Gilles Celeux, Cathy Maugis-Rabusseau, Mohammed Sedki
2018 Advances in Data Analysis and Classification  
In this paper, we propose an alternative regularization approach for variable selection in model-based clustering and classification.  ...  based clustering and classification. These make use of backward or forward procedures to define the roles of the variables.  ...  After a series of papers on variable selection in model-based clustering (Law et al, 2004; Tadesse et al, 2005; Raftery and Dean, 2006; Maugis et al, 2009a) , Maugis et al (2009b) proposed a general  ... 
doi:10.1007/s11634-018-0322-5 fatcat:wjttmzvjkvb23ecqx6s2a6sake

Variable selection in model-based discriminant analysis

C. Maugis, G. Celeux, M.-L. Martin-Magniette
2011 Journal of Multivariate Analysis  
This variable selection model was inspired by a previous work on variable selection in model-based clustering. A BIC-like model selection criterion is proposed.  ...  A general methodology for selecting predictors for Gaussian generative classification models is presented. The problem is regarded as a model selection problem.  ...  This modeling is the result of successive improvements of variable selection modeling in model-based clustering [18, 13, 14] .  ... 
doi:10.1016/j.jmva.2011.05.004 fatcat:k47dj3usojhvhk47ssfnftj6va

Robust variable selection for model-based learning in presence of adulteration [article]

Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
2020 arXiv   pre-print
In particular, several methods for variable selection in model-based classification have been proposed.  ...  In the present paper, we introduce two robust variable selection approaches: one that embeds a robust classifier within a greedy-forward selection procedure and the other based on the theory of maximum  ...  Gunter Ritter for the stimulating discussions and suggestions on how to transpose the ML subset selector approach, originally developed for clustering, to the classification framework.  ... 
arXiv:2007.14810v2 fatcat:ni3rtqut7bgddiaudv7tdvxuau

Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model

Jude Chibuike Nwadiuto, Hiroyuki Okuda, Tatsuya Suzuki
2021 Applied Sciences  
In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection.  ...  In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection.  ...  It should be noted that the PWARX model used in this work is a modified version of the general PWARX model presented above, in the sense that flexibility is allowed by variable selection in the number  ... 
doi:10.3390/app11114938 fatcat:sptteir2wfcw3gicyna7vmyiou

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Thomas Brendan Murphy, Nema Dean, Adrian E. Raftery
2010 Annals of Applied Statistics  
Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented.  ...  A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance.  ...  Model-based clustering with variable selection.  ... 
doi:10.1214/09-aoas279 pmid:20936055 pmcid:PMC2951685 fatcat:jnh5omxo65apxjiewqz2iojcnm

Variable Selection and Modeling of Drivers' Decision in Overtaking Behavior Based on Logistic Regression Model with Gazing Information

Jude C. Nwadiuto, Soichi YOSHINO, Hiroyuki OKUDA, Tatsuya SUZUKI
2021 IEEE Access  
In this sense, such a statistical methodology for model selection is valid if the selection of the input variables based on the statistical testing Their results also revealed that the model selection  ...  For drivers A, B, and D, the variable x 25 , which is the left-cluster viewing time is a common selection.  ... 
doi:10.1109/access.2021.3111753 fatcat:d4d2xnxkmne4lb6zc64uicotri

Investigating the status of successful aging based on selection, optimization and compensation model and its relationship with some demographic variables in elderly population of Shiraz, southwest of Iran, 2018

Giti Setoodeh, Maryam Hazrati, Farkhondeh Sharif, Sakineh Gholamzadeh
2020 Zenodo  
The objective of this study was to investigate the status of successful aging based on Selection, Optimization and Compensa­tion Model and its relationship with some demographic variables in the elderly  ...  population in Shiraz-Iran.Materials and Methods: In a cross-sectional study, 197 eligible elderly people were selected by multistage clus­ter sampling from four districts of Shiraz in 2018.  ...  Investi gating the status of successful aging based on selection, optimization and compensation model and its relationship with some demographic variables in elderly population of Shiraz, southwest of  ... 
doi:10.5281/zenodo.4079155 fatcat:b2mmh4ab6jblxpm7d7ekdvzsfi

Model-Based Clustering

Paul D. McNicholas
2016 Journal of Classification  
The notion of defining a cluster as a component in a mixture model was put forth by Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an important subfield of classification  ...  First, the definition of a cluster is discussed and some historical context for model-based clustering is provided.  ...  The availability of this software, together with the well-known review paper by Fraley and Raftery (2002b) , played an important role in popularizing model-based clustering.  ... 
doi:10.1007/s00357-016-9211-9 fatcat:ocdvpdp3ijb57egck4exbzzy7e

Model-based Clustering [article]

Bettina Grün
2018 arXiv   pre-print
They allow for an explicit definition of the cluster shapes and structure within a probabilistic framework and exploit estimation and inference techniques available for statistical models in general.  ...  In this chapter an introduction to cluster analysis is provided, model-based clustering is related to standard heuristic clustering methods and an overview on different ways to specify the cluster model  ...  At the core of cluster analysis is the definition of what a cluster is.  ... 
arXiv:1807.01987v1 fatcat:p4ceewdgpvaadmzxuelnjhn3sy
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