Canonical Variates in Wasserstein Metric Space
Researchers have developed a novel method for classifying data instances represented as distributions rather than single points. This approach utilizes the Wasserstein metric and introduces a dimension reduction technique based on maximizing the Fisher's ratio. The method iteratively optimizes transport and maximization steps, demonstrating improved classification accuracy and outperforming existing algorithms that use vector representations of distributional data. AI