Consistent regression of biophysical parameters with kernel methods

12/09/2020
by   Emiliano Diaz, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

08/05/2015

Bayesian Approximate Kernel Regression with Variable Selection

Nonlinear kernel regression models are often used in statistics and mach...
04/13/2021

Gradient Kernel Regression

In this article a surprising result is demonstrated using the neural tan...
02/20/2022

KLLR: A scale-dependent, multivariate model class for regression analysis

The underlying physics of astronomical systems governs the relation betw...
03/05/2018

A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression

Many machine learning problems can be formulated as predicting labels fo...
05/28/2018

Nonlinear Simplex Regression Models

In this paper, we propose a simplex regression model in which both the m...
01/30/2015

Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models

A novel algorithm is proposed to downscale microwave brightness temperat...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.