Artificial Intelligence #canonical variates#wasserstein metric
Canonical Variates in Wasserstein Metric Space: New Dimension Reduction Method Enhances Classification
A new paper by Jia and Lin proposes a dimension reduction method for classification when data are represented as distributions rather than points. By maximizing Fisher's ratio in Wasserstein metric space, the technique enhances classification performance and robustly handles distributional representations like Gaussian mixture models.
Jun 17, 2026 1 source