Geodesic Nonlinear Mapping Using the Neural Gas Network
The 2006 IEEE International Joint Conference on Neural Network ProceedingsPublished 1 January 2006
P. A. Estévez, Andrés M. Chong
Citations13
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TL;DR
The proposed geodesic nonlinear projection method based on self-organizing neural networks outperforms CDA and Isotop, in terms of the trustworthiness, continuity and topology preservation measures.
Abstract
A geodesic nonlinear projection method based on self-organizing neural networks is proposed. Firstly, the neural gas (NG) algorithm is used to obtain codebook vectors, and a graph is concurrently created by using the competitive Hebbian rule. Secondly, the nonlinear mapping is created by applying an adaptation rule for codebook positions in the projection space. The algorithm minimizes a cost function that favors the preservation of the local topology, using geodesic distances in the input space. The proposed method, called GNLP-NG, is an enhancement over curvilinear distance analysis (CDA). The mapping quality obtained with GNLP-NG outperforms CDA and Isotop, in terms of the trustworthiness, continuity and topology preservation measures.
Keywords
Computer Science
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