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New Wasserstein distance enhances Multidimensional Scaling for pattern recognition

By PulseAugur Editorial · [1 sources] ·

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

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Read on Hugging Face Daily Papers →

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New Wasserstein distance enhances Multidimensional Scaling for pattern recognition

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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Adjusted Wasserstein distances for bridging empirical and true distributions with applications to MDS

    This paper examines how metric adjustments to Multidimensional Scaling (MDS) can enhance its effectiveness as a visual tool for pattern recognition. The distance under consideration, referred to as Max-D-SW, is an adjustment of the Max-Sliced Wasserstein distance. In contrast to …

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