Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 563574
Title Large-scale random features for kernel regression
Author(s) Laparra, Valero; Gonzalez, Diego Marcos; Tuia, Devis; Camps-Valls, Gustau
Source In: 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. - Institute of Electrical and Electronics Engineers Inc. (International Geoscience and Remote Sensing Symposium (IGARSS) ) - ISBN 9781479979295 - p. 17 - 20.
Event IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, 2015-07-26/2015-07-31
DOI https://doi.org/10.1109/IGARSS.2015.7325686
Publication type Contribution in proceedings
Publication year 2015
Abstract

Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical retrieval of bio-geo-physical parameters. The RKS method allows to approximate a kernel matrix with a set of random bases sampled from the Fourier domain. We extend their use to other bases, such as wavelets, stumps, and Walsh expansions. We show that kernel regression is now possible for datasets with millions of examples and high dimensionality. Examples on atmospheric parameter retrieval from infrared sounders and biophysical parameter retrieval by inverting PROSAIL radiative transfer models with simulated Sentinel-2 data show the effectiveness of the technique.

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