Robust variable selection for model-based learning in presence of adulteration (Q830086)

scientific article; zbMATH DE number 7345450

Language	Label	Description	Also known as
English	Robust variable selection for model-based learning in presence of adulteration	scientific article; zbMATH DE number 7345450

Statements

instance of

scholarly article

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title

Robust variable selection for model-based learning in presence of adulteration (English)

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author

Andrea Cappozzo

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Francesca Greselin

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Thomas Brendan Murphy

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published in

Computational Statistics and Data Analysis

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publication date

7 May 2021

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full work available at URL

https://arxiv.org/abs/2007.14810

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zbMATH Keywords

variable selection

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model-based classification

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label noise

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outliers detection

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wrapper approach

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impartial trimming

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robust estimation

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describes a project that uses

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Variable selection for clustering and classification

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Model-Based Gaussian and Non-Gaussian Clustering

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Q3241581

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Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition

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Selection of relevant features and examples in machine learning

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Benchmark for filter methods for feature selection in high-dimensional classification data

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Model-based clustering of high-dimensional data: a review

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Model-Based Clustering and Classification for Data Science

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A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise

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

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Wild adaptive trimming for robust estimation and cluster analysis

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Rejoinder to the discussion of ``The power of monitoring: how to make the most of a contaminated multivariate sample''

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On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions

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Algorithms on Strings, Trees and Sequences

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Using Unlabelled Data to Update Classification Rules with Applications in Food Authenticity Studies

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A reweighting approach to robust clustering

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Variable selection methods for model-based clustering

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Model-Based Clustering, Discriminant Analysis, and Density Estimation

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A robust method for cluster analysis

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A general trimming approach to robust cluster analysis

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Exploring the number of groups in robust model-based clustering

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Best approximations to random variables based on trimming procedures

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Error Detecting and Error Correcting Codes

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Bayes Factors

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Wrappers for feature subset selection

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Computational Methods of Feature Selection

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Q3870155

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Variable Selection for Clustering with Gaussian Mixture Models

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

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Variable selection in model-based discriminant analysis

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Q4863621

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

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Robust fitting of mixtures using the trimmed likelihood estimator

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Analysis of new variable selection methods for discriminant analysis

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Variable Selection for Model-Based Clustering

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Least Median of Squares Regression

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Estimating the dimension of a model

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10.1162/153244303321897735

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Robust selection of variables in linear discriminant analysis

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Q3093222

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Class noise vs. attribute noise: A quantitative study of their impacts

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CSDA.2021.107186

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Sitelinks

Mathematics(1 entry)

mardi Publication:830086