Name
Title | ||
|---|---|---|
Principal Manifolds And Graphs In Practice: From Molecular Biology To Dynamical Systems |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1142/S0129065710002383 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | DocType | Volume |
Kohonen neural networks, self-organizing maps, principal manifolds, principal graphs, data visualization | Journal | 20 |
Issue | ISSN | Citations |
3 | 0129-0657 | 34 |
PageRank | References | Authors |
1.76 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander N. Gorban | 1 | 50 | 3.91 |
| Andrei Yu. Zinovyev | 2 | 93 | 11.87 |