@Article{AdrianaRomero2015,
author="Adriana Romero
and Petia Radeva
and Carlo Gatta",
title="Meta-parameter free unsupervised sparse feature learning",
journal="IEEE Transactions on Pattern Analysis and Machine Intelligence",
year="2015",
volume="37",
number="8",
pages="1716--1722",
abstract="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.",
optnote="MILAB; 600.068; 600.079; 601.160",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2594), last updated on Wed, 27 Jan 2016 08:48:53 +0100",
doi="10.1109/TPAMI.2014.2366129"
}