A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment
Tran, Dung T., Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2006, A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment, in ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 168-172.
To 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.
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ISBN
0769525210 9780769525211
ISSN
1051-4651
Language
eng
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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