Representing variables in the latent space
분석변수들의 잠재공간 표현

Myung-Hoe Huh
2017 Korean Journal of Applied Statistics  
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
more » ... ividual 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.
doi:10.5351/kjas.2017.30.4.555 fatcat:43i6ljbpr5ae5h2phfwrwz6uai