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Smart sampling and incremental function learning for very large high dimensional data

Loyola, Diego and Pedergnana, Mattia and Gimeno García, Sebastián (2016) Smart sampling and incremental function learning for very large high dimensional data. Neural Networks. Elsevier. DOI: 10.1016/j.neunet.2015.09.001. ISSN 0893-6080. (In Press)

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Official URL: http://dx.doi.org/10.1016/j.neunet.2015.09.001

Abstract

Very large high dimensional data are common nowadays and they impose new challenges to data-driven and data-intensive algorithms. Computational Intelligence techniques have the potential to provide powerful tools for addressing these challenges, but the current literature focuses mainly on handling scalability issues related to data volume in terms of sample size for classification tasks. This work presents a systematic and comprehensive approach for optimally handling regression tasks with very large high dimensional data. The proposed approach is based on smart sampling techniques for minimizing the number of samples to be generated by using an iterative approach that creates new sample sets until the input and output space of the function to be approximated are optimally covered. Incremental function learning takes place in each sampling iteration, the new samples are used to fine tune the regression results of the function learning algorithm. The accuracy and confidence levels of the resulting approximation function are assessed using the probably approximately correct computation framework. The smart sampling and incremental function learning techniques can be easily used in practical applications and scale well in the case of extremely large data. The feasibility and good results of the proposed techniques are demonstrated using benchmark functions as well as functions from real-world problems.

Document Type:Article
Title:Smart sampling and incremental function learning for very large high dimensional data
Authors:
AuthorsInstitution or Email of Authors
Loyola, DiegoDLR-IMF
Pedergnana, MattiaDLR-IMF
Gimeno García, SebastiánDLR-IMF
Date:2016
Journal or Publication Title:Neural Networks
Refereed publication:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.neunet.2015.09.001
Editors:
EditorsEmail
Doya, KenjiOkinawa Inst. of Science & Tech., Onna, Okinawa, Japan
Deliang, WangOhio State University, Columbus, Ohio, USA
Publisher:Elsevier
Series Name:2015 Special Issue
ISSN:0893-6080
Status:In Press
Keywords:High dimensional function approximation; Sampling discrepancy; Design of experiments; Probably approximately correct computation; Function learning; Neural networks
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Ozon-SAF
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Diego Loyola
Deposited On:25 May 2012 08:30
Last Modified:17 Dec 2015 17:28

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