| Autor: |
Barth, Timothy J., Griebel, Michael, Keyes, David E., Nieminen, Risto M., Roose, Dirk, Schlick, Tamar, Kégl, Balázs, Wunsch, Donald C., Gorban, Alexander N., Sumner, Neil R., Zinovyev, Andrei Y. |
| Zdroj: |
Principal Manifolds for Data Visualization & Dimension Reduction; 2007, p219-237, 19p |
| Abstrakt: |
Multidimensional data distributions can have complex topologies and variable local dimensions. To approximate complex data, we propose a new type of low-dimensional "principal object": a principal cubic complex. This complex is a generalization of linear and non-linear principal manifolds and includes them as a particular case. To construct such an object, we combine a method of topological grammars with the minimization of an elastic energy defined for its embedment into multidimensional data space. The whole complex is presented as a system of nodes and springs and as a product of one-dimensional continua (represented by graphs), and the grammars describe how these continua transform during the process of optimal complex construction [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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