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Abstract:
We present a probabilistic latent-variable framework for data
visualisation, a key feature of which is its applicability to
binary and categorical data types for which few established methods
exist. A variational approximation to the likelihood is exploited
to derive a fast algorithm for determining the model parameters.
Illustrations of application to real and synthetic binary data sets
are given
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