Fig. 1: A graphical illustration of the proposed method.

The algorithm takes as input the normalized pairwise distance matrices associated to a collection of candidate visualizations (viz1 to viz4) of a dataset. For each sample of the dataset, we compute the similarity matrix between the rows of the normalized distance matrices associated to the sample (rows highlighted in the same color), and then define the corresponding eigenscores as the first eigenvector of the similarity matrix. The size of the circles in the similarity matrices and the vectors of eigenscores indicate the magnitude of the entries (assumed to be non-negative). The meta-distance matrix is defined such that its rows are the eigenscore-weighted average of the rows in the normalized distance matrices. The meta-distance leads to a meta-visualization, expected to be more concordant with the underlying true structure than individual candidate visualizations.