TGMM: Fit the Tensor Gaussian Mixture Model (TGMM)

Description Usage Arguments Value Author(s) References Examples

View source: R/TGMM.R

Description

Fit the Tensor Gaussian Mixture Model (TGMM)

Usage

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TGMM(Xn, K, shape = "shared", initial = "kmeans", 
iter.max = 500, stop = 1e-3, trueY = NULL, print = FALSE)

Arguments

Xn

The tensor for clustering, should be array tpye, the last dimension is the sample size n.

K

Nuber of clusters, greater than or equal to 2.

shape

"shared" if assume common covariance across mixtures, "distinct" if allow different covariance structures. Default value is "shared".

initial

Initialization methed for the regularized EM algorithm. Default value is "kmeans".

iter.max

Maximum number of iterations. Default value is 500.

stop

Convergence threshold of relative change in cluster means. Default value is 1e-3.

trueY

A vector of true cluster labels of each observation. Default value is NULL.

print

Whether to print information including current iteration number, ralative change in cluster means and clustering error (%) in each iteration.

Value

id

A vector of estimated labels.

pi

Estimated cluster wight.

eta

A n by K matrix of estimated membership weights.

Mu.est

A list of estimated cluster means.

SIG.est

A list of estimated covariance matrices.

Author(s)

Kai Deng, Xin Zhang

References

Deng, K. and Zhang, X. (2020) Tensor Envelope Mixture Model For Simultaneous Clustering and Multiway Dimension Reduction, Manuscript.

Examples

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  A = array(c(rep(1,20),rep(2,20))+rnorm(40),dim=c(2,2,10))
  myfit = TGMM(A,K=2,shape="shared")

azuryee/TEMM documentation built on Dec. 31, 2020, 7:55 p.m.