Title:
Segmental Switching Linear Dynamic Systems
Segmental Switching Linear Dynamic Systems
| dc.contributor.author | Oh, Sang Min | |
| dc.contributor.author | Rehg, James M. | |
| dc.contributor.author | Dellaert, Frank | |
| dc.date.accessioned | 2006-04-21T17:46:34Z | |
| dc.date.available | 2006-04-21T17:46:34Z | |
| dc.date.issued | 2005 | |
| dc.description.abstract | 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. | en |
| dc.format.extent | 428541 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/9437 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.relation.ispartofseries | CC Technical Report; GIT-CC-05-13 | en |
| dc.subject | Probabilistic inference | |
| dc.subject | Time-series modeling | |
| dc.subject | Honeybee dance | |
| dc.subject | Segmental Switching Linear Dynamic Systems (S-SLDS) | |
| dc.title | Segmental Switching Linear Dynamic Systems | en |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Rehg, James M. | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.relation.ispartofseries | College of Computing Technical Report Series | |
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