@Misc{AdrianaRomero2014,
author="Adriana Romero
and Petia Radeva
and Carlo Gatta",
title="No more meta-parameter tuning in unsupervised sparse feature learning",
year="2014",
abstract="CoRR abs/1402.5766We 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.",
optnote="MILAB; LAMP; 600.079",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2471), last updated on Thu, 28 Jan 2021 10:25:59 +0100",
file=":http://refbase.cvc.uab.es/files/RRG2014.pdf:PDF"
}