(Georgia Institute of Technology, 2005)
Oh, Sang Min; Ranganathan, Ananth; Rehg, James M.; Dellaert, Frank
This paper aims to present a structured variational inference algorithm for switching linear dynamical systems (SLDSs) which was initially introduced by Pavlovic and Rehg. Starting with the need for the variational approach, we proceed to the derivation of the generic (model-independent) variational update formulas which are obtained under the mean field assumption. This leads us to the derivation of an approximate variational inference algorithm for an SLDS. The details of deriving the SLDS-specific variational update equations are presented.
(Georgia Institute of Technology, 2005)
Oh, Sang Min; Rehg, James M.; Dellaert, Frank
We introduce Segmental Switching Linear Dynamic Systems (S-SLDS), which improve on standard
SLDSs by explicitly incorporating duration modeling capabilities. We show that S-SLDSs can adopt
arbitrary finite-sized duration models that describe data more accurately than the geometric distributions
induced by standard SLDSs. We also show that we can convert an S-SLDS to an equivalent standard
SLDS with sparse structure in the resulting transition matrix. This insight makes it possible to adopt
existing inference and learning algorithms for the standard SLDS models to the S-SLDS framework. As
a consequence, the more powerful S-SLDS model can be adopted with only modest additional effort in
most cases where an SLDS model can be applied. The experimental results on honeybee dance decoding
tasks demonstrate the robust inference capabilities of the proposed S-SLDS model.