Online data visualization using the neural gas network
Abstract
A high-quality distance preserving output representation is provided to the neural gas (NG) network. The nonlinear mapping is determined concurrently along with the codebook vectors. The adaptation rule for codebook positions in the projection space minimizes a cost function that favors the trustworthy preservation of the local topology. The proposed visualization method, called OVI-NG, is an enhancement over curvilinear component analysis (CCA). The results show that the mapping quality obtained with OVI-NG outperforms the original CCA, in terms of the trustworthiness, continuity, topographic function and topology preservation measures. © 2006 Elsevier Ltd. All rights reserved.
Más información
Título según WOS: | Online data visualization using the neural gas network |
Título según SCOPUS: | Online data visualization using the neural gas network |
Título de la Revista: | NEURAL NETWORKS |
Volumen: | 19 |
Número: | 06-jul |
Editorial: | PERGAMON-ELSEVIER SCIENCE LTD |
Fecha de publicación: | 2006 |
Página de inicio: | 923 |
Página final: | 934 |
Idioma: | English |
URL: | http://linkinghub.elsevier.com/retrieve/pii/S0893608006000785 |
DOI: |
10.1016/j.neunet.2006.05.024 |
Notas: | ISI, SCOPUS |