This paper introduces an adjusted Wasserstein distance, termed Max-D-SW, designed to improve Multidimensional Scaling (MDS) for pattern recognition. The Max-D-SW method aggregates contributions from orthonormal bases, offering a numerical advantage over the original Max-Sliced Wasserstein distance, especially with heavy-tailed distributions. The research also provides sample-complexity bounds, demonstrating that Max-D-SW is statistically manageable and achieves comparable rates to its predecessor. However, the study notes that improved sample complexity for a metric does not always guarantee better performance when used in MDS. AI
RANK_REASON The item describes a new method presented in a research paper. [lever_c_demoted from research: ic=1 ai=0.4]
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