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) |
|---|---|
| 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 |
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Keywords
- Histogram observations
- Polytopes
- Principal components
ASJC Scopus subject areas
- Computer Science Applications
- Applied Mathematics
Cite this
Principal component analysis for histogram-valued data. / Le-Rademacher, Jennifer; Billard, L.
In: Advances in Data Analysis and Classification, Vol. 11, No. 2, 01.06.2017, p. 327-351.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Principal component analysis for histogram-valued data
AU - Le-Rademacher, Jennifer
AU - Billard, L.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
KW - Histogram observations
KW - Polytopes
KW - Principal components
UR - http://www.scopus.com/inward/record.url?scp=84969941388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969941388&partnerID=8YFLogxK
U2 - 10.1007/s11634-016-0255-9
DO - 10.1007/s11634-016-0255-9
M3 - Article
AN - SCOPUS:84969941388
VL - 11
SP - 327
EP - 351
JO - Advances in Data Analysis and Classification
JF - Advances in Data Analysis and Classification
SN - 1862-5347
IS - 2
ER -