Visualization and self-organization of multidimensional data through equalized orthogonal mapping
US6212509A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | May 2, 2000 |
| Grant date | Apr 3, 2001 |
| Priority date | — |
| Expiry date | May 2, 2020 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/2137
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The subject system provides reduced-dimension mapping of pattern data. Mapping is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively "topologically correct" low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficient at the same time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.