Principal component analysis for histogram-valued data

J. Le-Rademacher () and L. Billard ()
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J. Le-Rademacher: Mayo Clinic
L. Billard: University of Georgia

Advances in Data Analysis and Classification, 2017, vol. 11, issue 2, No 6, 327-351

Abstract: Abstract This paper introduces a principal component methodology for analysing histogram-valued data under the symbolic data domain. Currently, no comparable method exists for this type of data. The proposed method uses a symbolic covariance matrix to determine the principal component space. The resulting observations on principal component space are presented as polytopes for visualization. Numerical representation of the resulting polytopes via histogram-valued output is also presented. The necessary algorithms are included. The technique is illustrated on a weather data set.

Keywords: Principal components; Histogram observations; Polytopes; 62H25; 60-08 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-016-0255-9

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