On the one hand, we obtain low dimensional embeddings fitting the complexity of the target task.
first, the input layer is factorized into two components, the embeddings attained in unsupervised form, E, and the projection matrix S. Second, the size of the sub-space, in which the embeddings are projected, is much smaller than that of the original embeddings with typical reductions above one order of magnitude.