Figure 1: The scatterplots in (a) and (c) visualize the dimensionality reduced representations of two distinct subspaces of a high-dimensional dataset. The matrix visualization (b) shows the discrepancies between the distances in the two projections. The point's color in the projections encodes for data labels and serve as visual connection between them.

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We present a new approach to visualizing data that is well-suited for personal and casual applications. The idea is to map the data to another dataset that is already familiar to the user, and then rely on their existing knowledge to illustrate relationships in the data. We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical mapping is very flexible and allows us to adapt the visualization to its application and target audience. We present several examples where we map data to different domains and representations. This includes mapping data to cat images, encoding research interests with neural style transfer and representing movies as stars in the night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging and personalized visualizations. CCS CONCEPTS• Human-centered computing → Visualization techniques; Visualization theory, concepts and paradigms; • Computing methodologies → Machine learning.