TY - GEN
T1 - Learning context-awaremeasurementmodels
AU - Virani, Nurali
AU - Lee, Ji Woong
AU - Phoha, Shashi
AU - Ray, Asok
N1 - Publisher Copyright:
© 2015 American Automatic Control Council.
PY - 2015/7/28
Y1 - 2015/7/28
N2 - This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based mixture modeling method carries out this task in an unsupervised manner, and results in a machine-defined context set and a probability distribution on it. The resulting measurement model is guaranteed to have contextual awareness, in the sense that the measurements are mutually independent conditioned on the system state and context. Numerical examples illustrate how contextual awareness improves inference performance in the setting of sequential target detection.
AB - This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based mixture modeling method carries out this task in an unsupervised manner, and results in a machine-defined context set and a probability distribution on it. The resulting measurement model is guaranteed to have contextual awareness, in the sense that the measurements are mutually independent conditioned on the system state and context. Numerical examples illustrate how contextual awareness improves inference performance in the setting of sequential target detection.
UR - http://www.scopus.com/inward/record.url?scp=84940932002&partnerID=8YFLogxK
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U2 - 10.1109/ACC.2015.7172036
DO - 10.1109/ACC.2015.7172036
M3 - Conference contribution
AN - SCOPUS:84940932002
T3 - Proceedings of the American Control Conference
SP - 4491
EP - 4496
BT - ACC 2015 - 2015 American Control Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 American Control Conference, ACC 2015
Y2 - 1 July 2015 through 3 July 2015
ER -