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Found 6 papers, 2 papers with code
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation
Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator.
Large-scale optimal transport map estimation using projection pursuit
We theoretically show the proposed dimension reduction method can consistently estimate the most ``informative'' projection direction in each iteration.
Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors
Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate.
Sufficient dimension reduction for classification using principal optimal transport direction
The proposed method, named principal optimal transport direction (POTD), estimates the basis of the SDR subspace using the principal directions of the optimal transport coupling between the data respecting different response categories.
A Review on Modern Computational Optimal Transport Methods with Applications in Biomedical Research
Optimal transport has been one of the most exciting subjects in mathematics, starting from the 18th century.
Minimax Nonparametric Two-sample Test under Smoothing
Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.
