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0111 · Jun 202619922001200920172026
1 result for “Max-D-SW”
Adjusted Wasserstein distances for bridging empirical and true distributions with applications to MDSstat.ML

This paper improves MDS visualization by adjusting Wasserstein distances for heavy-tailed data.

problem Enhancing Multidimensional Scaling (MDS) for better pattern recognition with heavy-tailed distributions.
method Introduces Max-D-SW, a metric adjustment of Max-Sliced Wasserstein distance that aggregates over orthonormal bases.
result Max-D-SW provides a clear numerical advantage in MDS outcomes, especially for heavy-tailed distributions.
Multidimensional ScalingWasserstein distanceMax-D-SWHeavy-tailed distributionsPattern recognition
2026-06-29abs ↗pdf ↗