NDLI: Learning context-aware measurement models

Content Provider	IEEE Xplore Digital Library
Author	Virani, N. Ji-Woong Lee Phoha, S. Ray, A.
Copyright Year	2015
Description	Author affiliation: Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA (Virani, N.; Ray, A.) \|\| Appl. Res. Lab., Pennsylvania State Univ., University Park, PA, USA (Ji-Woong Lee; Phoha, S.)
Abstract	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.
Starting Page	4491
Ending Page	4496
File Size	184105
Page Count	6
File Format	PDF
e-ISBN	9781479986842
DOI	10.1109/ACC.2015.7172036
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Context Kernel Context modeling Support vector machines Mixture models Laplace equations Accuracy support vector regression Contextual awareness density estimation mixture models
Content Type	Text
Resource Type	Article

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