Adaptive kernel smoothing regression for spatio-temporal environmental datasets

Authors:
Federico Montesino Pouzols;Amaury Lendasse
Affiliations:
Department of Biosciences, University of Helsinki Biocenter 3, Viikinkaari 1, PO Box 65, FI-00014 University of Helsinki, Finland;Department of Information and Computer Science, Aalto University, Espoo, Finland and IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain and Computational Intelligence Group, Computer S ...
Venue:
Neurocomputing
Year:
2012

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Abstract

A method for performing kernel smoothing regression in an incremental, adaptive manner is described. A simple and fast combination of incremental vector quantization with kernel smoothing regression using adaptive bandwidth is shown to be effective for online modeling of environmental datasets. The approach proposed is to apply kernel smoothing regression in an incremental estimation of the (evolving) probability distribution of the incoming data stream rather than the whole sequence of observations. The method is illustrated on publicly available datasets corresponding to the Tropical Atmosphere Ocean array and the Helsinki Commission hydrographic database for the Baltic Sea.