Principal differences analysis: Interpretable characterization of differences between distributions
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions. The method operates by finding the projection that maximizes the Wasserstein divergence between the resulting univariate populations. Relying on the Cramer-Wold device, it requires no...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Neural Information Processing Systems Foundation, Inc.
2018
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| Online Access: | http://hdl.handle.net/1721.1/115931 https://orcid.org/0000-0002-7164-903X https://orcid.org/0000-0002-2199-0379 |