%0 Journal Article
%T Meta-parameter free unsupervised sparse feature learning
%A Adriana Romero
%A Petia Radeva
%A Carlo Gatta
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%D 2015
%V 37
%N 8
%F Adriana Romero2015
%O MILAB; 600.068; 600.079; 601.160
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2594), last updated on Wed, 27 Jan 2016 08:48:53 +0100
%X 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 CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well.
%U http://dx.doi.org/10.1109/TPAMI.2014.2366129
%P 1716-1722