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New Max-D-SW distance enhances Multidimensional Scaling for pattern recognition

By PulseAugur Editorial · [2 sources] ·

This paper introduces Max-D-SW, an adjusted version of the Max-Sliced Wasserstein distance, designed to improve Multidimensional Scaling (MDS) for pattern recognition. Max-D-SW aggregates contributions over orthonormal bases, offering a numerical advantage over the original formulation, especially with heavy-tailed distributions. The research also establishes sample-complexity bounds, demonstrating that Max-D-SW is statistically tractable and that improved sample complexity does not always guarantee better MDS performance. AI

IMPACT Introduces a novel metric that could improve pattern recognition in machine learning applications.

RANK_REASON Academic paper detailing a new statistical method and its application.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Max-D-SW distance enhances Multidimensional Scaling for pattern recognition

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Flor Martinez-Sermeno, Arturo Jaramillo, Johan Van Horebeek ·

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

    arXiv:2606.29665v1 Announce Type: new Abstract: 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 …

  2. arXiv stat.ML TIER_1 English(EN) · Johan Van Horebeek ·

    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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