Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)
Christopher Bishop, Markus Svensén, Christopher Williams
There is currently considerable interest in developing general non(cid:173) linear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying 'causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortu(cid:173) nately, to train such models generally requires large computational effort. In this paper we introduce a novel latent variable algorithm which retains the general non-linear capabilities of previous models but which uses a training procedure based on the EM algorithm. We demonstrate the performance of the model on a toy problem and on data from flow diagnostics for a multi-phase oil pipeline.