Principal component analysis for histogram-valued data

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)327-351
Number of pages25
JournalAdvances in Data Analysis and Classification
Issue number2
StatePublished - Jun 1 2017


  • Histogram observations
  • Polytopes
  • Principal components

ASJC Scopus subject areas

  • Computer Science Applications
  • Applied Mathematics


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