6533b823fe1ef96bd127ec7e

RESEARCH PRODUCT

Consistent Regression of Biophysical Parameters with Kernel Methods

Adrian Perez-suayGustau Camps-vallsEmiliano DiazValero Laparra

subject

FOS: Computer and information sciencesMathematical optimizationComputer Science - Machine Learning010504 meteorology & atmospheric sciences0211 other engineering and technologiesRegression analysisMachine Learning (stat.ML)02 engineering and technology01 natural sciencesRegressionData modelingMachine Learning (cs.LG)Set (abstract data type)Methodology (stat.ME)Nonlinear systemKernel methodConsistency (statistics)Statistics - Machine LearningKernel (statistics)Statistics - Methodology021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematics

description

This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are introduced, and both imply closed-form analytical solutions. The models exploit all the information from a set of drivers while being maximally independent of a set of auxiliary, protected variables. We successfully illustrate the performance in the estimation of chlorophyll content.

yearjournalcountryeditionlanguage
2020-12-09IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
10.1109/igarss.2018.8518504http://dx.doi.org/10.1109/igarss.2018.8518504