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Abstract:
We present an algorithm that infers the model structure of a
mixture of factor analysers using an efficient and deterministic
variational approximation to full Bayesian integration over model
parameters. This procedure can automatically determine the optimal
number of components and the local dimensionality of each component
(i.e. the number of factors in each factor analyser). Alternatively
it can be used to infer posterior distributions over number of
components and dimensionalities. Since all parameters are
integrated out, the method is not prone to overfitting. Using a
stochastic procedure for adding components, it is possible to
perform the variational optimisation incrementally and to avoid
local maxima. Results show that the method works very well in
practice and correctly infers the number and dimensionality of
nontrivial synthetic examples.
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