  The projected belief network (PBN) is a layered generative network (LGN) with
tractable likelihood function, and is based on a feed-forward neural network
(FFNN). There are two versions of the PBN: stochastic and deterministic
(D-PBN), and each has theoretical advantages over other LGNs. However,
implementation of the PBN requires an iterative algorithm that includes the
inversion of a symmetric matrix of size M X M in each layer, where M is the
layer output dimension. This, and the fact that the network must be always
dimension-reducing in each layer, can limit the types of problems where the PBN
can be applied. In this paper, we describe techniques to avoid or mitigate
these restrictions and use the PBN effectively at high dimension. We apply the
discriminatively aligned PBN (PBN-DA) to classifying and auto-encoding
high-dimensional spectrograms of acoustic events. We also present the
discriminatively aligned D-PBN for the first time.
