Discriminative Alignment of Projected Belief Networks
Paul M. Baggenstoss · IEEE Signal Processing Letters · 2021
The projected belief network (PBN) is a deep layered generative network with tractable likelihood function (LF) and can be used as a Bayesian classifier by training a separate model on each data class, and then classifying based on maximum likelihood (ML). Unlike other generative models with tractable LF, the PBN can share an embodiment and network parameters with a feed-forward classifier network. By training a PBN with a cost function that combines LF with classifier cross-entropy, its network weights can be ``aligned" to the decision boundaries separating the data class from other classes. This results in a Bayesian classifier that rivals state of the art discriminative classifiers. These claims are backed up by classification experiments involving spectrograms of spoken keywords and handwritten characters.