Treffer: Self-organization and missing values in SOM and GTM : Advances in Self-Organizing Maps

Title:
Self-organization and missing values in SOM and GTM : Advances in Self-Organizing Maps
Authors:
VATANEN, T, OSMALA, M, RAIKO, T, LAGUS, K, SYSI-AHO, M, ORESIC, M, HONKELA, T, LÄHDESMÄKI, H
Source:
Neurocomputing (Amsterdam). 147:60-70
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 37 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Algorithme Kohonen, Kohonen algorithm, Algoritmo Kohonen, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse donnée, Data analysis, Análisis datos, Autoorganisation, Self organization, Autoorganización, Base de données multidimensionnelle, Multidimensional database, Base dato multidimensional, Bibliothèque programme, Program library, Biblioteca programa, Carte topographique, Topographic map, Plano topográfico, Donnée manquante, Missing data, Dato que falta, Information incomplète, Incomplete information, Información incompleta, Initialisation, Initialization, Inicialización, Maniement donnée, Data handling, Manipulación dato, Réseau neuronal, Neural network, Red neuronal, Modèle génératif, Generative model, Modelo generativo, Visualisation donnée, Data visualization, Visualización de datos, Generative topographic mapping, Self-organization, Self-organizing map
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Aalto University School of Science, Department of Information and Computer Science, P.O. Box 15400, 00076 Aalto, Espoo, Finland
The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, United States
VTT Technical Research Centre of Finland, Espoo 02044, Finland
Steno Diabetes Center, 2820 Gentofte, Denmark
University of Helsinki, Department of modern languages, P.O. Box 24, 00014 Helsinki, Finland
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28836732
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28836732
Database:
PASCAL Archive

Weitere Informationen

In this paper, we study fundamental properties of the Self-Organizing Map (SOM) and the Generative Topographic Mapping (GTM), ramifications of the initialization of the algorithms and properties of the algorithms in the presence of missing data. We show that the commonly used principal component analysis (PCA) initialization of the GTM does not guarantee good learning results with high-dimensional data. Initializing the GTM with the SOM is shown to yield improvements in self-organization with three high-dimensional data sets: commonly used MNIST and ISOLET data sets and epigenomic ENCODE data set. We also propose a revision of handling missing data to the batch SOM algorithm called the Imputation SOM and show that the new algorithm is more robust in the presence of missing data. We benchmark the performance of the topographic mappings in the missing value imputation task and conclude that there are better methods for this particular task. Finally, we announce a revised version of the SOM Toolbox for Matlab with added GTM functionality.