| dc.creator |
Pascual-Marqui, Roberto D. |
|
| dc.creator |
Pascual-Montano, Alberto |
|
| dc.creator |
Kochi, Kieko |
|
| dc.creator |
Carazo, José M. |
|
| dc.date |
2008-01-18T08:36:32Z |
|
| dc.date |
2008-01-18T08:36:32Z |
|
| dc.date |
2007-08-22 |
|
| dc.date.accessioned |
2017-01-31T00:59:43Z |
|
| dc.date.available |
2017-01-31T00:59:43Z |
|
| dc.identifier |
European Patent Code: EP 1353295 B1 |
|
| dc.identifier |
http://hdl.handle.net/10261/2702 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2702 |
|
| dc.description |
Filing Date: 2000-12-12.-- International Publication Number WO_2002048962 (20020620) |
|
| dc.description |
This is a system for organizing n-dimensional data onto a lower dimensionality space in a non-linear and non-supervised manner. The types of methods presented here are usually known as self-organizing maps and are similar but not identical to the well known Kohonen self-organizing maps. The basic idea is a combination of data clustering and smooth projection thereof into a lower dimensional space (usually a two-dimensional grid). The proposed system consists of two modified versions of the functional of the well-known Fuzzy c-means clustering algorithm, where the cluster centers or code vectors are distributed on a low dimensional regular grid, for which a penalization term is added with the object of assuring a smooth distribution of the values of the code vectors on said grid. In one of the two cases, the faithfulness to the data is achieved by minimizing the differences between the data and the code vectors, and in the other case, the new functional is based on the probability density estimation of the input data. |
|
| dc.format |
809021 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.rights |
openAccess |
|
| dc.title |
Non-linear data mapping and dimensionality reduction system |
|
| dc.type |
Artículo |
|