Figure 2

Hierarchical clustering of the ICA and PCA outputs. (A) Hierarchical clustering of the ICA outputs with the last two 'common' components of matrix A removed. To display the cluster dendrogram conveniently, we transposed matrix A in the graph. That is, the columns of the graphical representation correspond to the rows of matrix A, denoting samples, and the rows of the graphical representation correspond to the columns of matrix A, denoting the ICA latent variables. (B) Hierarchical clustering of the principle components, with the number of the principle components k = 10. Similarly, the rows and columns of the graph denote the principle component and samples, respectively.