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— 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 lower-dimensional space such that the topological characteristics of the high-dimensional data are preserved. Recent work [6] [4](More)
— Nonlinear mapping is an approach of multidimen-sional 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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