Consistent regression of biophysical parameters with kernel methods

Autor:	Díaz, Emiliano, Pérez-Suay, Adrián, Laparra, Valero, Camps-Valls, Gustau
Rok vydání:	2020
Předmět:	Statistics - Methodology Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: substantial text overlap with arXiv:1710.05578
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.04922 Zobrazit plný text záznamu View this record from Arxiv