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Principal component analysis for histogram-valued data. (English) Zbl 1414.62213

Summary: 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.

MSC:

62H25 Factor analysis and principal components; correspondence analysis
60-08 Computational methods for problems pertaining to probability theory

Software:

SODAS
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References:

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