There is currently considerable interest in developing general non-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. Unfortunately, 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.
|Title of host publication||Advances in Neural Information Processing Systems 8|
|Editors||D. S. Touretzky, M. C. Mozer, M. E. Hasselmo|
|Place of Publication||Cambridge, MA|
|Number of pages||7|
|Publication status||Published - Jun 1996|
|Event||Advances in Neural Information Processing Systems 1996 - Hong Kong, China|
Duration: 12 Nov 1996 → 14 Nov 1996
|Conference||Advances in Neural Information Processing Systems 1996|
|Period||12/11/96 → 14/11/96|
Bibliographical noteCopyright of the Massachusetts Institute of Technology Press (MIT Press)
Bishop, C. M., Svens'en, M., & Williams, C. K. I. (1996). EM optimization of latent-variable density models. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8 (Vol. 8, pp. 465-471). MIT.