Towards Optimal Transport with Global Invariances
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Many problems in machine learning involve calculating correspondences between sets of objects, such as point clouds or images. Discrete optimal transport (OT) provides a natural and successful approach to such tasks whenever the two sets of objects can be represented in the same space or when we can evaluate distances between the objects. Unfortunately neither requirement is likely to hold when object representations are learned from data. Indeed, automatically derived representations such as word embeddings are typically fixed only up to some global transformations, for example, reflection or rotation. As a result, pairwise distances across the two types of objects are ill-defined without specifying their relative transformation. In this work, we propose a general framework for optimal transport in the presence of latent global transformations. We discuss algorithms for the specific case of orthonormal transformations, and show promising results in unsupervised word alignment.
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