FisherEM

R package FisherEM: The FisherEM Algorithm to Simultaneously Cluster and Visualize High-Dimensional Data. The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) <doi:10.1007/s11222-011-9249-9>, is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided.

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References in zbMATH (referenced in 2 articles , 1 standard article )

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  1. 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
  2. Jouvin, Nicolas; Bouveyron, Charles; Latouche, Pierre: A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering (2021)