Selection of a Representative Sample

Authors:
Herbert K. H. Lee;Matthew Taddy;Genetha A. Gray
Affiliations:
University of California, Santa Cruz, CA, USA and Baskin School of Engineering, Department of Applied Mathematics and Statistics, 1156 High Street, 1156 High Street, CA, 95064, USA;University of Chicago, Chicago, Illinois, USA;Sandia National Laboratories, Albuquerque, New Mexico, Livermore, California, USA
Venue:
Journal of Classification
Year:
2010

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Noisy data elimination using mutual k-nearest neighbor for classification mining

Journal of Systems and Software

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Abstract

Sometimes a larger dataset needs to be reduced to just a few points, and it is desirable that these points be representative of the whole dataset. If the future uses of these points are not fully specified in advance, standard decision-theoretic approaches will not work. We present here methodology for choosing a small representative sample based on a mixture modeling approach.