Principal component analysis for histogram-valued data

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)327-351
Number of pages25
JournalAdvances in Data Analysis and Classification
Volume11
Issue number2
DOIs
StatePublished - Jun 1 2017

Fingerprint

Covariance matrix
Principal component analysis
Histogram
Principal Component Analysis
Principal Components
Visualization
Polytopes
Weather
Necessary
Methodology
Output

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 journalArticle

@article{0a8664e28ae3448ebe5df3d5a3066b84,
title = "Principal component analysis for histogram-valued data",
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 = "Histogram observations, Polytopes, Principal components",
author = "Jennifer Le-Rademacher and L. Billard",
year = "2017",
month = "6",
day = "1",
doi = "10.1007/s11634-016-0255-9",
language = "English (US)",
volume = "11",
pages = "327--351",
journal = "Advances in Data Analysis and Classification",
issn = "1862-5347",
publisher = "Springer Verlag",
number = "2",

}

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 -