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Representing variables in the latent space
분석변수들의 잠재공간 표현
2017
Korean Journal of Applied Statistics
분석변수들의 잠재공간 표현
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
doi:10.5351/kjas.2017.30.4.555
fatcat:43i6ljbpr5ae5h2phfwrwz6uai