in this paper, we study supervised learning tasks on the space of probability measures. we approach this problem by embedding the space of probability measures into $l^2$ spaces using the optimal transport framework. in the embedding spaces, regular machine learning techniques are used to achieve linear separability. this idea has proved successful in applications and when the classes to be separated are generated by shifts and scalings of a fixed measure. this paper extends the class of elementary transformations suitable for the framework to families of shearings, describing conditions under which two classes of sheared distributions can be linearly separated. we furthermore give necessary bounds on the transformations to achieve a pre-specified separation level, and show how multiple embeddings can be used to allow for larger families of transformations. we demonstrate our results on image classification tasks. based on joint work with caroline moosmueller, harish kannan, and alex cloninger.