Abstract
In this paper we review algorithmic aspects related to maximum-supportplane partitions. These partitions have been defined in Bock (1992) and analyzed in Pötzelberger and Strasser (2001). The local approach to inference leads to a certain subclass of partitions. They are obtained from quantizing the distribution of the score function. We propose a numerical method to compute these partitions B approximately, in the sense that they are asymptotically optimal for increasing sizes |B|. These findings are based on recent results on the asymptotic distribution of sets of prototypes.
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Pötzelberger, K. (2002). Quantization of Models: Local Approach and Asymptotically Optimal Partitions. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_10
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DOI: https://doi.org/10.1007/978-3-642-56181-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43691-1
Online ISBN: 978-3-642-56181-8
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