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.
Original language | English (US) |
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Pages (from-to) | 327-351 |
Number of pages | 25 |
Journal | Advances in Data Analysis and Classification |
Volume | 11 |
Issue number | 2 |
DOIs | |
State | Published - Jun 1 2017 |
Keywords
- Histogram observations
- Polytopes
- Principal components
ASJC Scopus subject areas
- Applied Mathematics
- Statistics and Probability
- Computer Science Applications