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No more meta-parameter tuning in unsupervised sparse feature learning
Preprint
24 February 2014
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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 STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.