期刊
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
卷 20, 期 3, 页码 219-232出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065710002383
关键词
Kohonen neural networks; self-organizing maps; principal manifolds; principal graphs; data visualization
We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.
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