We propose a novel optimization-based approach to embedding heterogeneous high-dimensional data characterized by a graph. The goal is to create a two-dimensional visualization of the graph structure such that edge-crossings are minimized while preserving proximity relations between nodes. This paper provides a fundamentally new approach for addressing the… (More)

Figure 3: Embeddings for randomly generated graph in R7 with 50 nodes and 80 edges using (a) Stress majorization (stress=131.8, 369 crossings) and (b) CR-SM (stress=272.1, 0 crossings). The original planar embedding had stress=352.5.