A Comparative Study of Supervised Learning Techniques for the Radiative Transfer Equation Inversion

Esteban Garcia, Fernando De la Torre Frade, and A. De Castro
International Conference on Machine Learning and Data Analysis, October, 2007.


Abstract
Estimation of the constituents of a gas (e.g. temperature, concentration) from high resolution spectroscopic measurements is a fundamental step to control and improve the efficiency of combustion processes governed by the Radiative Transfer Equation (RTE). Typically such estimation is performed using thermocouples; however, these sensors are intrusive and must undergo the harsh furnace environment. In this paper, we follow a machine learning approach to learn the relation between the spectroscopic measurements and gas constituents such as temperature, concentration and length. This is a challenging problem due to the non-linear behavior of the RTE and the high dimensional data obtained from sensor measurements. We perform a comparative study of linear and neural network regression models, using canonical correlation analysis (CCA), principal component analysis (PCA), reduced rank regression(RRR), and kernel canonical correlation (KCCA) to reduce the dimensionality.

Keywords
radiateive transfer equation, component analysis, supervised learning

Notes
Associated Project(s): Component Analysis for Data Analysis

Text Reference
Esteban Garcia, Fernando De la Torre Frade, and A. De Castro, "A Comparative Study of Supervised Learning Techniques for the Radiative Transfer Equation Inversion," International Conference on Machine Learning and Data Analysis, October, 2007.

BibTeX Reference
@inproceedings{Garcia_2007_5847,
   author = "Esteban Garcia and Fernando {De la Torre Frade} and A. De Castro",
   title = "A Comparative Study of Supervised Learning Techniques for the Radiative Transfer Equation Inversion",
   booktitle = "International Conference on Machine Learning and Data Analysis",
   month = "October",
   year = "2007",
}