FisherEM: The FisherEM Algorithm to Simultaneously Cluster and Visualize High-Dimensional Data

The FisherEM algorithm, proposed by Bouveyron & Brunet (201) <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.

Package details

AuthorCharles Bouveyron and Camille Brunet
MaintainerCharles Bouveyron <>
Package repositoryView on CRAN
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FisherEM documentation built on May 1, 2019, 7:56 p.m.