Winkley, Jonathan, Jiang, Ping and Hossain, Alamgir (2011) Dimension reduction for linear separation with curvilinear distances. In: 17th International Conference on Soft Computing, 15 June - 17 June 2011, Brno, Czech Republic.
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Abstract
Any high dimensional data in its original raw form may contain obviously classifiable clusters which are difficult to identify given the high-dimension representation. In reducing the dimensions it may be possible to perform a simple classification technique to extract this cluster information whilst retaining the overall topology of the data set. The supervised method presented here takes a high dimension data set consisting of multiple clusters and employs curvilinear distance as a relation between points, projecting in a lower dimension according to this relationship. This representation allows for linear separation of the non-separable high dimensional cluster data and the classification to a cluster of any successive unseen data point extracted from the same higher dimension.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | G400 Computer Science |
| Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
| Depositing User: | EPrint Services |
| Date Deposited: | 05 Aug 2011 08:54 |
| Last Modified: | 17 Dec 2023 11:50 |
| URI: | https://nrl.northumbria.ac.uk/id/eprint/2070 |
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