Dimension reduction for linear separation with curvilinear distances

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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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)
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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