Journal article
Landmark-embedded Gaussian process with applications for functional data modeling
IISE transactions, Vol.52(11), pp.1033-1046
10/27/2021
DOI: 10.1080/24725854.2021.1974129
Abstract
In practice, we often need to infer the value of a target variable from functional observation data. A challenge in this task is that the relationship between the functional data and the target variable is very complex: the target variable not only influences the shape but also the location of the functional data. In addition, due to the uncertainties in the environment, the relationship is probabilistic, that is, for a given fixed target variable value, we still see variations in the shape and location of the functional data. To address this challenge, we present a landmark-embedded Gaussian process model that describes the relationship between the functional data and the target variable. A unique feature of the model is that landmark information is embedded in the Gaussian process model so that both the shape and location information of the functional data are considered simultaneously in a unified manner. Gibbs-Metropolis-Hasting algorithm is used for model parameters estimation and target variable inference. The performance of the proposed framework is evaluated by extensive numerical studies and a case study of nano-sensor calibration.
Details
- Title: Subtitle
- Landmark-embedded Gaussian process with applications for functional data modeling
- Creators
- Jaesung Lee - University of Wisconsin–MadisonChao Wang - University of IowaXiaoyu Sui - University of ChicagoShiyu Zhou - University of Wisconsin–MadisonJunhong Chen - Argonne National Laboratory
- Resource Type
- Journal article
- Publication Details
- IISE transactions, Vol.52(11), pp.1033-1046
- DOI
- 10.1080/24725854.2021.1974129
- ISSN
- 2472-5854
- eISSN
- 2472-5862
- Publisher
- Taylor & Francis
- Grant note
- DOI: 10.13039/100000001, name: National Science Foundation, award: 1727846, 2039268; DOI: 10.13039/100000147, name: Division of Civil, Mechanical and Manufacturing Innovation
- Language
- English
- Electronic publication date
- 10/27/2021
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984204079102771
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