Representing variables in the latent space
- Authors
- Huh, Myung-Hoe
- Issue Date
- 8월-2017
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- data visualization; clustering of variables; latent variables; principal component analysis; biplot; supplementary variables
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.30, no.4, pp.555 - 566
- Indexed
- KCI
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 30
- Number
- 4
- Start Page
- 555
- End Page
- 566
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/82740
- DOI
- 10.5351/KJAS.2017.30.4.555
- ISSN
- 1225-066X
- Abstract
- For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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