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Visualizing multidimensional data in multiple groups

Authors
Huh, Myung-Hoe
Issue Date
2월-2017
Publisher
KOREAN STATISTICAL SOC
Keywords
canonical discriminant analysis; principal component analysis; biplot; Mahalanobis distance; scaled Euclidean distance
Citation
KOREAN JOURNAL OF APPLIED STATISTICS, v.30, no.1, pp.83 - 93
Indexed
KCI
Journal Title
KOREAN JOURNAL OF APPLIED STATISTICS
Volume
30
Number
1
Start Page
83
End Page
93
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/84851
DOI
10.5351/KJAS.2017.30.1.083
ISSN
1225-066X
Abstract
A typical approach to visualizing k (>= 2)-group multidimensional data is to use Fisher's canonical discriminant analysis (CDA). CDA finds the best low-dimensional subspace that accommodates k group centroids in the Mahalanobis space. This paper proposes an alternative visualization procedure functioning in the Euclidean space, which finds the primary dimension with maximum discrimination of k group centroids and the secondary dimension with maximum dispersion of all observational units. This hybrid procedure is especially useful when the number of groups k is two.
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