Auralia I. Edwards

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Large datasets consisting of high-dimensional vectors commonly describe complex objects. Having these vectors exist in a smaller dimension where the topological characteristics of the original space are preserved, allows clusters or patterns inherent in the data to be identified. This paper investigates the capability of various Particle Swarm Optimisation(More)
Reducing the dimensionality of high-dimensional data simplifies how data is presented, allowing easier visualisation of high-dimensional data and facilitating more efficient extraction of knowledge. Nonlinear mapping methods transform data existing in high-dimensional space into a lower-dimensional space such that the topological characteristics of the(More)
Reducing the dimensionality of high-dimensional data allows easier visualisation of data, facilitating more efficient extraction of knowledge. Nonlinear mapping methods transform data existing in high-dimensional space into a lowerdimensional space such that the topological characteristics of the high-dimensional data are preserved. Recent work [6] [4](More)
Nonlinear mapping is an approach of multidimensional scaling where a high-dimensional space is transformed into a lower-dimensional space such that the topological characteristics of the original high-dimensional space are preserved. This enables visualisation and feature extraction of datasets. Problems exist in conventional mapping methods in that they(More)
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