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In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer‐extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full‐field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003)] and ‐distributed stochastic neighbor embedding (‐SNE) [L. van der Maaten and G. Hinton, “Visualizing data using t‐SNE,” J. Mach. Learn. Res. 9, 2579–2605 (2008)].

These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional …