phung-aprobabilisticmodel-2006.pdf (166.83 kB)
A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment
conference contribution
posted on 2006-01-01, 00:00 authored by D Tran, Quoc-Dinh Phung, H Bui, Svetha VenkateshSvetha VenkateshTo tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.
History
Event
International Conference on Pattern Recognition (18th : 2006 : Hong Kong, China)Pagination
168 - 172Publisher
IEEELocation
Hong Kong, ChinaPlace of publication
Washington, D. C.Start date
2006-08-20End date
2006-08-24ISSN
1051-4651ISBN-13
9780769525211ISBN-10
0769525210Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
2006, IEEETitle of proceedings
ICPR 2006 : Proceedings of the 18th International Conference on Pattern RecognitionUsage metrics
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