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Large-scale random features for kernel regression


Laparra, Valero; Gonzalez, Diego Marcos; Tuia, Devis; Camps-Valls, Gustau (2015). Large-scale random features for kernel regression. In: Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, Milan, 26 July 2015 - 31 July 2015, 17-20.

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.

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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Item Type:Conference or Workshop Item (Paper)["page:refereed_set_notrefereed" not defined]["page:subtype_original" not defined]
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:31 July 2015
Deposited On:23 Mar 2018 14:53
Last Modified:13 Apr 2018 11:47
Publisher:IEEE
ISBN:978-1-4799-7929-5
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/IGARSS.2015.7325686
Official URL:http://ieeexplore.ieee.org/document/7325686/

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