  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.
