Large-scale random features for kernel regression

Valero Laparra, Diego Marcos Gonzalez, Devis Tuia, Gustau Camps-Valls

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781479979295
Publication statusPublished - 10 Nov 2015
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: 26 Jul 201531 Jul 2015

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015


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