Method for classification of newly arrived multidimensional data points in dynamic big data sets
US9147162B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 15, 2013 |
| Grant date | Sep 29, 2015 |
| Priority date | — |
| Expiry date | Apr 6, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/353
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for classification of a newly arrived multidimensional data point (MDP) in a dynamic data uses multi-scale extension (MSE). The multi-scale out-of-sample extension (OOSE) uses a coarse-to-fine hierarchy of the multi-scale decomposition of a Gaussian kernel that established the distances between MDPs in a training set to find the coordinates of newly arrived MDPs in an embedded space. A well-conditioned basis is first generated in a source matrix of MDPs. A single-scale out-of-sample extension (OOSE) is applied to the newly arrived MDP on the well-conditioned basis to provide coordinates of an approximate location of the newly arrived MDP in an embedded space. A multi-scale OOSE is then applied to the newly arrived MDP to provide improved coordinates of the newly arrived MDP location in the embedded space.
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