High-dimensional setting Structured prediction tasks often have much more features than simple bag-of-words representation, and performance relies on the rare features.
the corrupted input is divided to S subsets We obtain a projection matrix W s for each subset by reconstructing the pivot features from the features in this subset; we can then use the sum of all reconstructions as the new features, tanh( S s=1 W s X s ).