Title: High Dimensionnal Supervised Classification and Clustering

Description: New disciminant analysis and data clustering methods for high dimensional data, based on the asumption 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.

Title: High Dimensional Supervised Classification and Clustering
Author: Laurent Bergé, Charles Bouveyron and Stéphane Girard
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

Author: Laurent Berge, Charles Bouveyron and Stephane Girard
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.

Depends: grDevices, utils, graphics, stats, MASS

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
Packaged: 2016-12-20 10:59:55 UTC; laurent.berge

Packaged: 2018-05-11 12:19:08 UTC; laurent.berge

Packaged: 2019-11-19 15:38:22 UTC; laurent.berge

Title: Clustering High Dimensional Data with Hidden Markov Model on
person("Yevhen", "Tupikov", email = "yzt116@psu.edu", role = c("aut")),
person("Lin", "Lin", email = "llin@psu.edu", role = "aut"),
person("Lixiang", "Zhang", email = "lzz46@psu.edu", role = "aut"),
person("Jia", "Li", email = "jiali@psu.edu", role = c("aut", "cre")))
Description: Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <http://jmlr.org/papers/v18/16-342.html>.
Collate: 'AllClass.R' 'AllGeneric.R' 'AllMethod.R' 'clustControl.R'
Packaged: 2019-04-11 18:32:02 UTC; zhanglixiang