Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM)

Anastase Alexandre Charantonis; Pierre Testor; Laurent Mortier; Fabrizio D'Ortenzio; Sylvie Thiria

doi:10.1016/j.procs.2015.05.496

Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM)

Anastase Alexandre Charantonis, Pierre Testor, Laurent Mortier, Fabrizio D'Ortenzio, Sylvie Thiria

Research output: Contribution to journal › Conference article › peer-review

Abstract

We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in a highly correlated multidimensional dataset. This method was applied for the completion of a complex oceanographic data-set containing glider data from the EYE of the Levantine experiment of the EGO project. ITCOMP SOM provided reconstructed temperature and salinity profiles that are consistent with the physics of the phenomenon they sampled. A cross-validation test was performed and validated the approach, providing a root mean square error of providing a root mean square error of 0,042°C for the reconstruction of the temperature profiles and 0,008 PSU for the simultaneous reconstruction of the salinity profiles.

Original language	English
Pages (from-to)	2198-2206
Number of pages	9
Journal	Procedia Computer Science
Volume	51
Issue number	1
DOIs	https://doi.org/10.1016/j.procs.2015.05.496
Publication status	Published - 1 Jan 2015
Event	International Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands Duration: 21 Apr 2002 → 24 Apr 2002

Keywords

Data completion
EYE of the Levantine
Gliders
Iterative method
Multi-dimensional data
Salinity profiles
Self-organizing maps
Similarity function
Temperature profiles

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10.1016/j.procs.2015.05.496

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title = "Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM)",

abstract = "We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in a highly correlated multidimensional dataset. This method was applied for the completion of a complex oceanographic data-set containing glider data from the EYE of the Levantine experiment of the EGO project. ITCOMP SOM provided reconstructed temperature and salinity profiles that are consistent with the physics of the phenomenon they sampled. A cross-validation test was performed and validated the approach, providing a root mean square error of providing a root mean square error of 0,042°C for the reconstruction of the temperature profiles and 0,008 PSU for the simultaneous reconstruction of the salinity profiles.",

keywords = "Data completion, EYE of the Levantine, Gliders, Iterative method, Multi-dimensional data, Salinity profiles, Self-organizing maps, Similarity function, Temperature profiles",

author = "Charantonis, \{Anastase Alexandre\} and Pierre Testor and Laurent Mortier and Fabrizio D'Ortenzio and Sylvie Thiria",

note = "Publisher Copyright: {\textcopyright} The Authors. Published by Elsevier B.V.; International Conference on Computational Science, ICCS 2002 ; Conference date: 21-04-2002 Through 24-04-2002",

year = "2015",

month = jan,

day = "1",

doi = "10.1016/j.procs.2015.05.496",

language = "English",

volume = "51",

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journal = "Procedia Computer Science",

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T1 - Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM)

AU - Charantonis, Anastase Alexandre

AU - Testor, Pierre

AU - Mortier, Laurent

AU - D'Ortenzio, Fabrizio

AU - Thiria, Sylvie

N1 - Publisher Copyright: © The Authors. Published by Elsevier B.V.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in a highly correlated multidimensional dataset. This method was applied for the completion of a complex oceanographic data-set containing glider data from the EYE of the Levantine experiment of the EGO project. ITCOMP SOM provided reconstructed temperature and salinity profiles that are consistent with the physics of the phenomenon they sampled. A cross-validation test was performed and validated the approach, providing a root mean square error of providing a root mean square error of 0,042°C for the reconstruction of the temperature profiles and 0,008 PSU for the simultaneous reconstruction of the salinity profiles.

AB - We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in a highly correlated multidimensional dataset. This method was applied for the completion of a complex oceanographic data-set containing glider data from the EYE of the Levantine experiment of the EGO project. ITCOMP SOM provided reconstructed temperature and salinity profiles that are consistent with the physics of the phenomenon they sampled. A cross-validation test was performed and validated the approach, providing a root mean square error of providing a root mean square error of 0,042°C for the reconstruction of the temperature profiles and 0,008 PSU for the simultaneous reconstruction of the salinity profiles.

KW - Data completion

KW - EYE of the Levantine

KW - Gliders

KW - Iterative method

KW - Multi-dimensional data

KW - Salinity profiles

KW - Self-organizing maps

KW - Similarity function

KW - Temperature profiles

U2 - 10.1016/j.procs.2015.05.496

DO - 10.1016/j.procs.2015.05.496

M3 - Conference article

AN - SCOPUS:84939155055

SN - 1877-0509

VL - 51

SP - 2198

EP - 2206

JO - Procedia Computer Science

JF - Procedia Computer Science

IS - 1

T2 - International Conference on Computational Science, ICCS 2002

Y2 - 21 April 2002 through 24 April 2002

ER -

Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM)

Abstract

Keywords

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