mmppca: mmppca

In VBmix: Variational Bayesian Mixture Models

Description

estimates the variational posterior distribution of a MPPCA that aggregates a collection of input MPPCA models. A lower bound is calculated and monitored at each iteration. This posterior can be used for various purposes (e.g. MC proposal distribution). It can be transformed using mppcaToGmm and subMppca, outputing a GMM. The maximal rank of output factor matrices is determined by the inputs.

Usage

mmppca(mods, ncomp, thres = 0.1, maxit = NULL)

Arguments

mods	input MPPCA that concatenates the set of components to aggregate.
ncomp	number of components in the posterior.
thres	threshold for lower bound variations between 2 iterations. Convergence is decided if this variation is below thres.
maxit	if NULL, the stopping criterion is related to thres. If not NULL, maxit iterations are performed.

Value

estimated posterior MPPCA with ncomp components.

Author(s)

Pierrick Bruneau

References

Bruneau, P., Gelgon, M. and Picarougne, F. (2010) _Aggregation of probabilistic PCA mixtures with a variational-Bayes technique over parameters_, ICPR'10.

Bruneau, P., Gelgon, M. and Picarougne, F. (2011) _Component-level aggregation of probabilistic PCA mixtures using variational-Bayes_, Tech Report, http://hal.archives-ouvertes.fr/docs/00/56/72/99/PDF/techrep.pdf.

See Also

newMppca mppca subMppca

Examples

temp <- newMppca()
for(i in 1:3) temp <- appendToMppca(temp, pcapen[[i]])
temp2 <- mmppca(temp, 50, maxit=30)

VBmix documentation built on May 30, 2017, 2:34 a.m.