Abstract
In dealing with high-dimensional, large data, for the sake of abstract generation one resorts to either dimensionality reduction or cluster the patterns and deal with cluster representatives or both. The current paper examines whether there exists an equivalence in terms of generalization error. Four different approaches are followed and results of exercises are provided in driving home the issues involved.
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Babu, T.R., Murty, M.N., Agrawal, V.K. (2005). On Simultaneous Selection of Prototypes and Features in Large Data. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_94
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DOI: https://doi.org/10.1007/11590316_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
Online ISBN: 978-3-540-32420-1
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