6533b872fe1ef96bd12d2dc7

RESEARCH PRODUCT

Semi-Supervised Support Vector Biophysical Parameter Estimation

Jordi Munoz-mariJavier Calpe-maravillaGustau Camps-vallsLuis Gómez-chova

subject

Artificial neural networkbusiness.industryComputer scienceEstimation theoryPattern recognitionRegression analysisSupport vector machineStatistics::Machine LearningKernel (linear algebra)Kernel methodVariable kernel density estimationPolynomial kernelRadial basis function kernelArtificial intelligencebusinessLaplace operator

description

Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.

yearjournalcountryeditionlanguage
2008-01-01IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
https://doi.org/10.1109/igarss.2008.4779554