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.
- Arturo Jaramillo
- arXiv
- Hugging Face
- Max-D-SW
- Max-Sliced Wasserstein distance
- Multidimensional Scaling
- alphaXiv
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- DagsHub
- Gotit.pub
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