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	Adriana Romero, Petia Radeva, & Carlo Gatta. (2015). Meta-parameter free unsupervised sparse feature learning. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 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. Permanent link \| Save citation: RTF PDF LaTeX \| Export record: BibTeX Endnote ISI RIS Atom XML MODS XML ODF XML Word XML
	Adriana Romero, Petia Radeva, & Carlo Gatta. (2014). No more meta-parameter tuning in unsupervised sparse feature learning. Abstract: CoRR abs/1402.5766 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. Permanent link \| Save citation: RTF PDF LaTeX \| Export record: BibTeX Endnote ISI RIS Atom XML MODS XML ODF XML Word XML

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