Multilinear Dynamical Systems for Tensor Time Series

Mark Rogers, Lei Li, Stuart J. Russell
2013 Neural Information Processing Systems  
Data in the sciences frequently occur as sequences of multidimensional arrays called tensors. How can hidden, evolving trends in such data be extracted while preserving the tensor structure? The model that is traditionally used is the linear dynamical system (LDS) with Gaussian noise, which treats the latent state and observation at each time slice as a vector. We present the multilinear dynamical system (MLDS) for modeling tensor time series and an expectation-maximization (EM) algorithm to
more » ... imate the parameters. The MLDS models each tensor observation in the time series as the multilinear projection of the corresponding member of a sequence of latent tensors. The latent tensors are again evolving with respect to a multilinear projection. Compared to the LDS with an equal number of parameters, the MLDS achieves higher prediction accuracy and marginal likelihood for both artificial and real datasets.
dblp:conf/nips/RogersLR13 fatcat:dgrqc6ok7ffgjbodcoodgjd3xe