Model-based regression clustering for high-dimensional data: application to functional data

DOI10.1007/s11634-016-0242-1zbMath1414.62238arXiv1409.1333OpenAlexW2253831701MaRDI QIDQ2418303

Emilie Devijver

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1409.1333

zbMATH Keywords

model-based clusteringregressionhigh-dimensionfunctional data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

On Cluster-Aware Supervised Learning: Frameworks, Convergent Algorithms, and Applications ⋮ Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data ⋮ Lasso in Infinite dimension: application to variable selection in functional multivariate linear regression ⋮ Wavelet estimation of the dimensionality of curve time series ⋮ A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models ⋮ Mixtures of stochastic differential equations with random effects: application to data clustering

Uses Software

• Matlab
• fda (R)
• Wavelet Toolbox
• CAPUSHE

Cites Work

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• Statistics for high-dimensional data. Methods, theory and applications.
• \(\ell_{1}\)-penalization for mixture regression models
• Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
• Slope heuristics: overview and implementation
• Finite mixture regression: a sparse variable selection by model selection for clustering
• On the convergence properties of the EM algorithm
• Reduced-rank regression for the multivariate linear model
• Wavelet-based scalar-on-function finite mixture regression models
• Low rank multivariate regression
• Minimal penalties for Gaussian model selection
• Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Functional mixture regression
• A theory for multiresolution signal decomposition: the wavelet representation
• The Bayesian Lasso
• Clustering for Sparsely Sampled Functional Data
• Stability Selection
• Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions
• Convergence of a block coordinate descent method for nondifferentiable minimization