R package HDclassif: High Dimensional Supervised Classification and Clustering. Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Anderlucci, Laura; Fortunato, Francesca; Montanari, Angela: High-dimensional clustering via random projections (2022)
  2. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  3. Jouvin, Nicolas; Bouveyron, Charles; Latouche, Pierre: A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering (2021)
  4. Kim, Nam-Hwui; Browne, Ryan P.: In the pursuit of sparseness: a new rank-preserving penalty for a finite mixture of factor analyzers (2021)
  5. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  6. Sarkar, Shuchismita; Zhu, Xuwen; Melnykov, Volodymyr; Ingrassia, Salvatore: On parsimonious models for modeling matrix data (2020)
  7. Cipolli, William III; Hanson, Timothy: Supervised learning via smoothed Polya trees (2019)
  8. Houdard, Antoine; Bouveyron, Charles; Delon, Julie: High-dimensional mixture models for unsupervised image denoising (HDMI) (2018)
  9. Tortora, Cristina; McNicholas, Paul D.; Browne, Ryan P.: A mixture of generalized hyperbolic factor analyzers (2016)
  10. Nia, Vahid Partovi; Davison, Anthony C.: A simple model-based approach to variable selection in classification and clustering (2015)
  11. Rémi Lebret; Serge Iovleff; Florent Langrognet; Christophe Biernacki; Gilles Celeux; Gérard Govaert: Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library (2015) not zbMATH
  12. Sylla, Seydou N.; Girard, Stéphane; Diongue, Abdou Ka; Diallo, Aldiouma; Sokhna, Cheikh: A classification method for binary predictors combining similarity measures and mixture models (2015)
  13. Arnošt Komárek; Lenka Komárková: Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data (2014) not zbMATH
  14. Bouveyron, Charles; Brunet-Saumard, Camille: Model-based clustering of high-dimensional data: a review (2014)
  15. Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Amorim, Dinani: Do we need hundreds of classifiers to solve real world classification problems? (2014)
  16. Jacques, Julien; Preda, Cristian: Functional data clustering: a survey (2014)
  17. Laurent Bergé; Charles Bouveyron; Stéphane Girard: HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data (2012) not zbMATH
  18. Przemyslaw Biecek; Ewa Szczurek; Martin Vingron; Jerzy Tiuryn: The R Package bgmm: Mixture Modeling with Uncertain Knowledge (2012) not zbMATH
  19. Vahid Nia; Anthony Davison: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust (2012) not zbMATH