A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

Neurocomputing

Volumn 74, Issue 16, 2011, Pages 2624-2632

  Delgado, Soledad ^a   Gonzalo, Consuelo ^a   Martinez, Estibaliz ^a   Arquero, Agueda ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Delaunay triangulation; Growing cell structures; Self organizing map; Topology preserving; Visualization methods

Indexed keywords

COMBINED MEASURE; DELAUNAY TRIANGULATION; FEATURE MAP; GROWING CELL STRUCTURES; GROWING SELF ORGANIZING MAP; HIGH DIMENSIONAL DATA VISUALIZATION; HIGH-DIMENSIONAL; INPUT DATAS; INPUT SIGNAL; INPUT SPACE; KOHONEN; LOW QUALITIES; MISCONFIGURATIONS; NETWORK TOPOLOGY; NEURAL NETWORK MODEL; NONPARAMETRIC METHODS; QUANTITATIVE METHOD; SELF ORGANIZING; SOM ALGORITHMS; SOM NETWORKS; TOPOLOGICAL STRUCTURE; TOPOLOGY PRESERVATION; TOPOLOGY PRESERVING; TRAINING PARAMETERS; VISUALIZATION METHODS;

CLUSTERING ALGORITHMS; CONFORMAL MAPPING; DATA VISUALIZATION; ELECTRIC NETWORK TOPOLOGY; INPUT OUTPUT PROGRAMS; SELF ORGANIZING MAPS; VECTOR QUANTIZATION; VISUALIZATION;

TOPOLOGY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; INFORMATION PROCESSING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUALITY CONTROL; QUANTITATIVE ANALYSIS; SELF ORGANIZING MAP; SIGNAL PROCESSING;

EID: 80051602071     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.03.021     Document Type: Article

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