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Wasserstein discriminant analysis
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DOI：10.1007/s10994-018-5717-1.png)
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Abstract
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Wasserstein discriminant analysis (WDA) is a new supervised linear dimensionality reduction algorithm. Following the blueprint of classical Fisher Discriminant Analysis, WDA selects the projection matrix that maximizes the ratio of the dispersion of projected points pertaining to different classes and the dispersion of projected points belonging to a same class. To quantify dispersion, WDA uses regularized Wasserstein distances. Thanks to the underlying principles of optimal transport, WDA is able to capture both global (at distribution scale) and local (at samples' scale) interactions between classes. In addition, we show that WDA leverages a mechanism that induces neighborhood preservation. Regularized Wasserstein distances can be computed using the Sinkhorn matrix scaling algorithm; the optimization problem of WDA can be tackled using automatic differentiation of Sinkhorn's fixed-point iterations. Numerical experiments show promising results both in terms of prediction and visualization on toy examples and real datasets such as MNIST and on deep features obtained from a subset of the Caltech dataset.
Keywords:
Linear discriminant analysis
Optimal transport
Wasserstein distance
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