HDclassif

High Dimensional Supervised Classification and Clustering

Last update: 23 August 2023

Copyright license: GNU General Public License, version 2.0

Software version identifier: 2.2.0, 1.0, 1.1.1, 1.1.2, 1.1.3, 1.1, 1.2.1, 1.2.2, 1.2.3, 1.2, 1.3, 2.0.2, 2.0, 2.1.0, 2.2.1

Source code repository: https://github.com/cran/HDclassif

Description

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.

Related Items (18)

Model-based clustering of high-dimensional data: a review ⋮ High-dimensional clustering via random projections ⋮ Anderson relaxation test for intrinsic dimension selection in model-based clustering ⋮ Hands-On Machine Learning with R ⋮ Functional data clustering: a survey ⋮ A classification method for binary predictors combining similarity measures and mixture models ⋮ In the pursuit of sparseness: a new rank-preserving penalty for a finite mixture of factor analyzers ⋮ Unnamed Item ⋮ On parsimonious models for modeling matrix data ⋮ A mixture of generalized hyperbolic factor analyzers ⋮ pheble ⋮ Supervised learning via smoothed Polya trees ⋮ A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering ⋮ High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI) ⋮ Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series ⋮ Unnamed Item ⋮ Gaussian mixture model with an extended ultrametric covariance structure ⋮ A simple model‐based approach to variable selection in classification and clustering

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