Abstract
We have shown how to think about high-dimensional spaces as multi-centered spaces, and we have introduced the algorithmic basis for constructing a skeleton for such a space. We have also seen how to divide up a space into qualitative regions that allow outliers and small clusters to be assessed and interpreted in terms of what their impact on existing models should be.
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Skillicorn, D.B. (2012). Using Models of High-Dimensional Spaces. In: Understanding High-Dimensional Spaces. SpringerBriefs in Computer Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33398-9_7
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DOI: https://doi.org/10.1007/978-3-642-33398-9_7
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33397-2
Online ISBN: 978-3-642-33398-9
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