No more meta-parameter tuning in unsupervised sparse feature learning
  We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. 
Adriana Romero, Petia Radeva and Carlo Gatta
cs.LG cs.CV