By filtering out unimportant details and noises, we expect the low dimensional mapping can capture the intrinsic structure of the input data that is discriminative for the classification task and generalizable across domains.
we learn such a mapping function by conducting supervised word clustering on the labeled documents using a hierarchical multinomial Naive Bayes model.