 Sparse autoencoder may include more (rather than fewer) hidden units than inputs, but only a small number of the hidden units are allowed to be active at once.This sparsity constraint forces the model to respond to the unique statistical features of the input data used for training.Specifically, a sparse autoencoder is an autoencoder whose training criterion involves a sparsity penalty formula_27 on the code layer formula_28.