Description Usage Arguments Value Author(s) References See Also Examples
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.
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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. |
estimated posterior MPPCA with ncomp components.
Pierrick Bruneau
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.
newMppca mppca subMppca
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