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Large-scale random features for kernel regression


Laparra, Valero; Marcos-Gonzalez, Diego; Tuia, Devis; Camps-Valls, Gustau (2015). Large-scale random features for kernel regression. In: IGARSS 2015, Milan (Italy), 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 inver- sion 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 expan- sions. 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 inver- sion 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 expan- sions. 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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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
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:14 Jan 2016 08:58
Last Modified:08 Dec 2017 16:56
Publisher:IEEE
ISBN:978-1-4799-7929-5
Free access at:Official URL. An embargo period may apply.
Official URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7325686

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