Aggregated Wasserstein Metric

• Aggregated Wasserstein metric is a distance measure that aggregates distributional information to compare statistical differences in complex models.
• It is applied in machine learning, notably in HMM state registration, to improve alignment accuracy across hidden states.
• Experimental results suggest that the AW metric offers promising performance improvements over traditional Wasserstein approaches.

Searching arXiv for the Aggregated Wasserstein metric and closely related usages of the abbreviation “AW.” Searching arXiv for Aggregated Wasserstein HMM state registration.